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  Subjects -> ELECTRONICS (Total: 155 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 5)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 7)
Advances in Microelectronic Engineering     Open Access   (Followers: 9)
Advances in Power Electronics     Open Access   (Followers: 19)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 176)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 22)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Archives of Electrical Engineering     Open Access   (Followers: 11)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 25)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 30)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 9)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 40)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 13)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 86)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 69)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 53)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 56)
Electronics Letters     Hybrid Journal   (Followers: 23)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 39)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 2)
Energy Storage Materials     Full-text available via subscription   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 12)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 5)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 118)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 49)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 38)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 26)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 7)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 44)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 38)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 46)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 13)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 29)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 10)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 20)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 46)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 6)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 13)
IET Power Electronics     Hybrid Journal   (Followers: 22)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 16)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 9)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 28)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 6)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 8)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 15)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 7)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 17)
International Journal of Antennas and Propagation     Open Access   (Followers: 9)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 1)
International Journal of Control     Hybrid Journal   (Followers: 14)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 11)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 6)
International Journal of Nanoscience     Hybrid Journal   (Followers: 2)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
International Journal of Power Electronics     Hybrid Journal   (Followers: 12)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 7)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 5)
International Journal on Communication     Full-text available via subscription   (Followers: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 7)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 6)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 15)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 5)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 3)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 109)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 6)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 9)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 2)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 30)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 7)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 18)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 14)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 6)
Metrology and Measurement Systems     Open Access   (Followers: 4)
Microelectronics and Solid State Electronics     Open Access   (Followers: 13)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 32)
Nanotechnology, Science and Applications     Open Access   (Followers: 3)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access   (Followers: 4)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 13)
Paladyn, Journal of Behavioral Robotics     Open Access  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 5)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 3)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 46)
Semiconductors and Semimetals     Full-text available via subscription  
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 1)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 54)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 9)
Solid-State Electronics     Hybrid Journal   (Followers: 6)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 1)
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 5)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 5)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [46 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Information for Authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Institutional Listings
    • Abstract: Presents institutional listings relating to this publication.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Frontcover
    • Abstract: Presents the front cover of this issue of the publication.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • In-Memory Parallel Processing of Massive Remotely Sensed Data Using an
           Apache Spark on Hadoop YARN Model
    • Authors: Wei Huang;Lingkui Meng;Dongying Zhang;Wen Zhang;
      Pages: 3 - 19
      Abstract: MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to in-memory datasets of Spark, the shortages are eliminated. To facilitate implementation and assure high performance of Spark-based algorithms in a complex cloud computing environment, a strip-oriented parallel programming model is proposed. By incorporating strips of RS data with resilient distributed datasets (RDDs) of Spark, all-level parallel RS algorithms can be easily expressed with coarse-grained transformation primitives and BitTorrent-enabled broadcast variables. Additionally, a generic image partition method for Spark-based RS algorithms to efficiently generate differentiable key/value strips from a Hadoop distributed file system (HDFS) is implemented for concealing the heterogeneousness of RS data. Data-intensive multitasking algorithms and iteration-intensive algorithms were evaluated on a Hadoop yet another resource negotiator (YARN) platform. Experiments indicated that our Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image took less than 260 s. Further, the efficiency of iteration-intensive algorithms is insensitive to image size.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • GPU Projection of ECAS-II Segmenter for Hyperspectral Images Based on
           Cellular Automata
    • Authors: Javier López-Fandiño;Blanca Priego;Dora B. Heras;Francisco Argüello;
      Pages: 20 - 28
      Abstract: Segmentation is a key issue in the processing of multidimensional images such as those in the field of remote sensing. Most of the segmentation algorithms developed for multidimensional images begin by reducing the dimensionality of the images, thus loosing information that could be relevant in the segmentation process. Evolutionary cellular automata segmentation (ECAS-II) is an evolutionary approach that provides cellular automata-based segmenters considering all the spectral information contained in a hyperspectral image without applying any technique for dimensionality reduction. This paper presents an efficient graphics processor unit implementation of the type of segmenters produced by ECAS-II for land cover hyperspectral images. The method is evaluated over remote sensing hyperspectral images, introducing it on a spectral–spatial classification scheme based on extreme learning machines. Experiments have shown that the proposed approach achieves better accuracy results for land cover purposes than other spectral–spatial classification techniques based on segmentation.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Semantics-Enabled Framework for Spatial Image Information Mining of Linked
           Earth Observation Data
    • Authors: Kuldeep R. Kurte;Surya S. Durbha;Roger L. King;Nicolas H. Younan;Rangaraju Vatsavai;
      Pages: 29 - 44
      Abstract: Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework is proposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Fusion of Sun-Synchronous and Geostationary Images for Coastal and Ocean
           Color Survey Application to OLCI (Sentinel-3) and FCI (MTG)
    • Authors: Cécile Peschoud;Audrey Minghelli;Sandrine Mathieu;Manchun Lei;Ivane Pairaud;Christel Pinazo;
      Pages: 45 - 56
      Abstract: Open ocean and coastal area monitoring requires multispectral satellite images with a middle spatial resolution ({\sim 300 {text{m}}}) and a high temporal repeatability ({\sim 1 {text{h}}}) . As no current satellite sensors have such features, the aim of this study is to propose a fusion method to merge images delivered by a low earth orbit (LEO) sensor with images delivered by a geostationary earth orbit (GEO) sensor. This fusion method, called spatial spectral temporal fusion (SSTF), is applied to the future sensors—Ocean and Land Color Instrument (OLCI) (on Sentinel-3) and Flexible Combined Imager (FCI) (on Meteosat Third Generation) whose images were simulated. The OLCI bands, acquired at t0, are divided by the oversampled corresponding FCI band acquired at t0 and multiplied by the FCI bands acquired at t1. The fusion product is used for the next fusion at t1 and so on. The high temporal resolution of FCI allows its signal-to-noise ratio (SNR) to be enhanced by the means of temporal filtering. The fusion quality indicator ERGAS computed between SSTF fusion products and reference images is around 0.75, once the FCI images are filtered from the noise and 1.08 before filtering. We also compared the estimation of chlorophyll (Chl), suspended particulate matter (SPM), and colored dissolved organic matter (CDOM) maps from the fusion products with the input simulation maps. The comparison shows an average relative errors on Chl, SPM, and CDOM, respectively, of 64.6%, 6.2%, and 9.5% with the SSTF method. The SSTF method was also compared with an existing fusion method called the spati l and temporal adaptive reflectance fusion model (STARFM).
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Shifting Trends in Bimodal Phytoplankton Blooms in the North Pacific and
           North Atlantic Oceans From Space With the Holo-Hilbert Spectral Analysis
    • Authors: Min Zhang;Yuanling Zhang;Fangli Qiao;Jia Deng;Gang Wang;
      Pages: 57 - 64
      Abstract: Merged satellite ocean color data were used to examine trends in the timing and magnitude of phytoplankton blooms. Special emphasis was placed on the peak shift of spring and autumn/winter blooms in the North Pacific and North Atlantic Oceans with bimodal seasonal cycles. Ensemble empirical mode decomposition and Holo-Hilbert spectral analysis were combined to extract seasonal signals and investigate their modulation at multiple time-scales. In the temperate North Atlantic Ocean, earlier and decreasing spring blooms were detected with delayed and increased autumn blooms. The temperate North Pacific Ocean presented delayed and increased spring and autumn blooms, with the delay in fall blooms was larger than spring ones. The separation between two bloom peaks was increasing in both temperate North Atlantic and Pacific regions. The intrinsic variation in bloom timing and magnitude in these selected regions could be clearly extracted by ensemble empirical mode decomposition. Further Holo-Hilbert spectral results showed that changes in the annual cycle and bloom characteristics were modulated by interannual variability. These results suggest the critical role of interannual variability in the modulation of phytoplankton seasonality.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Ocean Surface Wind Retrieval Using SMAP L-Band SAR
    • Authors: Xuan Zhou;Jinsong Chong;Xiaofeng Yang;Wei Li;Xiaoxuan Guo;
      Pages: 65 - 74
      Abstract: The soil moisture active passive (SMAP) L-band synthetic aperture radar (SAR) could continuously provide global km scale ocean surface wind observations, which had a better coverage than other SARs and a higher spatial resolution than scatterometers. This paper investigates SMAP normalized radar cross sections (NRCS) dependence on wind vectors using more than 5 million matchups consisting of Defense Meteorological Satellite Program F17 Special Sensor Microwave Image/Sounder wind speed, National Center for Environmental Predication wind direction and SMAP L-band NRCS. An L-band geophysical model function (GMF) is proposed for SMAP wind retrieval on the basis of these matchups, and it indicates wind speed and direction dependence of SMAP L-band NRCS for about 40° incidence angle and 0–25 m/s wind speed range in both HH and VV polarization. The wind speed dependence increases rapidly with wind speed, and HH-polarized one is greater than VV polarization. The upwind–downwind difference for HH polarization is greater than that for VV polarization. A negative upwind–crosswind asymmetry occurs for HH- and VV-polarized backscatter at lower wind speeds. The retrieved SMAP wind speed using the proposed GMF is validated by using National Data Buoy Center buoy winds. The root mean square differences and biases are 1.77 and 0.19 m/s, respectively. The accuracies of SMAP wind speeds at 0–10 m/s range are better than those at higher wind speed range. In addition, SMAP wind speeds in upwind and downwind directions are relatively more accurate than those in crosswind directions.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Distinguishing Heavy-Metal Stress Levels in Rice Using Synthetic Spectral
           Index Responses to Physiological Function Variations
    • Authors: Ming Jin;Xiangnan Liu;Ling Wu;Meiling Liu;
      Pages: 75 - 86
      Abstract: Accurately assessing the heavy-metal contamination in crops is crucial to food security. This study provides a method to distinguish heavy-metal stress levels in rice using the variations of two physiological functions as discrimination indices, which are obtained by assimilation of remotely sensed data with a crop growth model. Two stress indices, which correspond to daily total text{CO}_{2} assimilation and dry-matter conversion coefficient were incorporated into the World Food Study (WOFOST) crop growth model and calculated by assimilating the model with leaf area index (LAI), which was derived from time-series HJ1-CCD data. The stress levels are not constant with rice growth; thus, to improve the reliability, the two stress indices were obtained at both the first and the latter half periods of rice growth. To compare the stress indices of different stress levels, a synthetic stress index was established by combining the two indices; then, three types of stress index discriminant spaces based on the synthetic index of different growth periods were constructed, in which the two-dimensional discriminant space based on two growth periods showed the highest accuracy, with a misjudgment rate of 4.5%. When the discrimination rules were applied at a regional scale, the average correct discrimination rate was 95.0%.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Differentiating Tree and Shrub LAI in a Mixed Forest With ICESat/GLAS
           Spaceborne LiDAR
    • Authors: Jinyan Tian;Le Wang;Xiaojuan Li;Chen Shi;Huili Gong;
      Pages: 87 - 94
      Abstract: Leaf area index (LAI) is an important descriptor of many biological and physical processes of vegetation. However, the challenges associated with differentiating tree and shrub LAI (tsLAI) have hindered research in mixed forests. Being the first spaceborne LiDAR system, geoscience laser altimeter system (GLAS) has demonstrated its advantage in collecting extensive forest structure information. In this study, we aimed to estimate tsLAI in a mixed forest with GLAS. The refined Levenberg–Marquardt algorithm for Gaussian decomposition was implemented to decompose GLAS data into ground and multiple vegetation signals, within which it is hypothesized that each vegetation signal corresponds to a particular height layer. Subsequently, the height of each layer was extracted through the decomposed GLAS signals, and a height threshold method to distinguish trees from shrubs was developed. Then, a tsLAI-specific ratio defined as ground-to-total energy return of the GLAS signal was calculated, and tsLAI was predicted by a linear regression model established from field measurements and the ratio. Finally, a study site in Ejina, China, where the dominant species are Populus euphratica (tree) and Tamarix ramosissima (shrub) was used to calibrate and validate the methods. Compared with the field measurement LAI, GLAS-predicted LAI presented a high agreement in which R2 , RMSE, and %RMSE of trees were determined to be 0.797, 0.087, and 19.176, respectively. In contrast, R2 , RMSE, and %RMSE of shrubs were found to be 0.676, 0.081, and 21.825, respectively. Overall, our study provided a feasible and effective approach for estimating tsLAI with GLAS over a flat region.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • An Intensity Gradient/Vegetation Fractional Coverage Approach to Mapping
           Urban Areas From DMSP/OLS Nighttime Light Data
    • Authors: Minghong Tan;
      Pages: 95 - 103
      Abstract: Many studies have demonstrated the efficient extraction of the spatial extent of urban areas from Defense Meteorological Satellite Program/Operational Linescan System imagery using a fixed thresholding technique. These studies may underestimate and overestimate the extents of small and large cities, respectively. To overcome this problem, a new intensity gradient (IG) and vegetation fractional coverage (VFC) method is developed for identifying cities or towns, principally based on the assumption that there is a border around a city at which the nighttime light intensity decreases sharply. Using this method, the spatial extents of urban areas for two of the biggest countries in the world, namely China and the United States, were extracted in 2010. The urban areas thus identified are compared with the urban areas interpreted from Landsat Thematic Mapper imagery, and the results show that there is a significant linear relationship between the former and latter areas. This demonstrates that the IG/VFC model is effective for efficiently extracting the extent of urban areas from nighttime light imagery.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite
           Data to Improve Crop Biomass Estimation
    • Authors: Taifeng Dong;Jiangui Liu;Budong Qian;Qi Jing;Holly Croft;Jingming Chen;Jinfei Wang;Ted Huffman;Jiali Shang;Pengfei Chen;
      Pages: 104 - 117
      Abstract: Maximum light use efficiency ( {text{LUE}}_{\rm{\max }} ) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop {text{LUE}}_{\rm{\max }} from satellite remote sensing data. {text{LUE}}_{\rm{\max }} at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. {text{LUE}}_{\rm{\max }} was then mapped using the best regression model from a VI. The influence factors on {text{LUE}}_{\rm{\max }} variability were also assessed. Contrary to a fixed {text{LUE}}_{\rm{\max }} , our results suggest that {text{LUE}}_{\rm{\max }} is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level {text{LUE}}_{\rm{\max }} and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to deri-e {text{LUE}}_{\rm{\max }} from remote sensing observations. To evaluate the quality of {text{LUE}}_{\rm{\max }} derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant {text{LUE}}_{\rm{\max }} and the derived variable {text{LUE}}_{\rm{\max }} . Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable {text{LUE}}_{\rm{\max }} . This study offers a new way to derive {text{LUE}}_{\rm{\max }} for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Radar Signal Penetration and Horizons Detection on Europa Through
           Numerical Simulations
    • Authors: Federico Di Paolo;Sebastian E. Lauro;Davide Castelletti;Giuseppe Mitri;Francesca Bovolo;Barbara Cosciotti;Elisabetta Mattei;Roberto Orosei;Claudia Notarnicola;Lorenzo Bruzzone;Elena Pettinelli;
      Pages: 118 - 129
      Abstract: We propose a strategy to evaluate the performance of a radar sounder for the subsurface exploration of the Europa icy crust and, in particular, the possibility to detect liquid water at the base of the ice shell. The approach integrates the information coming from experimental measurements of the dielectric properties of icy materials, thermal models related to different crustal scenarios, and numerical simulations of radar signal propagation. The radar response has been evaluated in terms of cumulative attenuation, signal-to-noise ratio (SNR), and reflectivity. Our simulations indicate that a subsurface radar operating at 9 MHz can identify shallow-buried targets and to detect the ice/water interface in various thermal scenarios. Under our assumptions the ice/water interface can be detected almost down to a depth of 15 km under a conductive ice shell, whereas for a convective ice shell, the maximum depth is about 12 km (in the cold downwelling plume). We also discuss the possibility to detect shallow targets associated with interfaces between pure water ice and text{MgSO}_{4} \cdot 11 text{H}_{2}{\rm O} ice mixtures at various salt contents, using the data of laboratory dielectric measurements.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Quantifying the Termination Mechanism Along the North Tabriz-North Mishu
           Fault Zone of Northwestern Iran via Small Baseline PS-InSAR and GPS
           Decomposition
    • Authors: Zhe Su;Er-Chie Wang;Jyr-Ching Hu;Morteza Talebian;Sadra Karimzadeh;
      Pages: 130 - 144
      Abstract: Quantitative understanding of stress transfer between major fault systems can elucidate the kinematics of large-scale plate interactions. This study analyzed right-lateral strike-slip motion on the North Tabriz fault (NTF) in an area where this structure appears to transition into a thrust fault known as the North Mishu fault (NMF). These faults play an important but cryptic role in accommodating stress related to the Arabia-Eurasia plate collision. We analyzed regional velocity vectors from permanent and temporary GPS arrays to estimate changes in fault-parallel and fault-normal slip rates in the transition zone between the NTF and NMF. Independent of its compressional motion, the NMF exhibits a dextral strike-slip rate of ∼2.62 mm/yr. Along the NTF, the right-lateral slip rate decreases and the vertical slip rate on increases at rates of 0.08 and 0.38 mm/yr km, respectively, as the NTF approaches the NMF. This study also used small baseline (SBAS) PS-InSAR results to reveal a NE-SW-striking reverse fault and a developing syncline hidden beneath the Tabriz Basin. Additionally, we calculated the vertical displacement rates using horizontal vectors from the GPS data and mean line-of-sight rate estimates from the SBAS data. While the study area does not express large-scale extrusion, such as that observed in the Anatolian Plate, the transformation of strike-slip motion into thrusting and crustal shortening along the NMF-NTF fault zone accommodates most of the N–S compression affecting the northwestern Iranian Plateau. In this region, small-sized, right-lateral strike-slip faults, and other folded structures form horsetail features. These dispersed structures accommodate eastward extrusion of the northwestern Iranian Plateau.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • High-Level Feature Selection With Dictionary Learning for Unsupervised SAR
           Imagery Terrain Classification
    • Authors: Jiawei Chen;Licheng Jiao;Zaidao Wen;
      Pages: 145 - 160
      Abstract: Features are of great importance for synthetic aperture radar (SAR) imagery terrain classification, but low-level features usually readily suffer from the speckle noise and they are incapable or inaccurate to capture some complex and irregular texture structure. In this paper, a novel feature learning framework is proposed to address this problem, in which some mid-level and high-level features are simultaneously learned by exploiting the spatial context constraints and sparse priors. More specifically, the mid-level features served as the intermediates are extracted from several initialized low-level features by the spatial constraints to reduce the influence of the speckle noise. Then, more abstract and discriminative high-level features are learned with an effective dictionary learning algorithm so as to represent the complex structures in SAR imagery. Finally, both artificial synthesis and real SAR imagery are utilized to verify the effectiveness of the proposed framework. It is demonstrated from both quantitative evaluations and visual results that the proposed algorithm performs better than other compared algorithms and the learned high-level feature is robust to the speckle noise and can improve the classification performance.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • A Two-Dimensional Beam-Steering Method to Simultaneously Consider Doppler
           Centroid and Ground Observation in GEOSAR
    • Authors: Jianlai Chen;Guang-Cai Sun;Mengdao Xing;Jun Yang;Zhenyu Li;Guobin Jing;
      Pages: 161 - 167
      Abstract: Due to Earth’s rotation and an elliptical satellite orbit, large Doppler centroids along satellite orbit inevitably occur in geosynchronous Earth orbit synthetic aperture radar (GEOSAR). Nonzero Doppler centroid causes a large range migration, which complicates the data acquisition and design of imaging algorithms. Thus, beam steering is used to decrease the centroid. At the same time, the ground observation of interest is prerequisite for applications. Thus, a unique two-dimensional (2-D) beam-steering method to simultaneously consider the reduction of Doppler centroid and ground observation for the GEOSAR is studied. The minimum-Doppler plane is proposed to minimize the centroid and to guarantee the beams that illuminate the area of interest. Subsequently, to achieve required ground coverage, beam directions determined by the minimum-Doppler plane are slightly adjusted. The method has been validated through the simulation of two types of orbits.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Focus Improvement for High-Resolution Highly Squinted SAR Imaging Based on
           2-D Spatial-Variant Linear and Quadratic RCMs Correction and
           Azimuth-Dependent Doppler Equalization
    • Authors: Dong Li;Huan Lin;Hongqing Liu;Guisheng Liao;Xiaoheng Tan;
      Pages: 168 - 183
      Abstract: The results of the linear range cell migration (RCM) correction and inherent range-dependent squint angle in the case of high-resolution highly squinted synthetic aperture radar (SAR) imaging produce two-dimensional (2-D) spatial-variant RCMs and azimuth-dependent Doppler parameters (i.e., highly varying Doppler centroid and frequency modulation rates), which make highly squinted SAR imaging difficult. However, the most existing algorithms failed to consider these problems. To obtain high-quality SAR image, in this study, both the 2-D spatial-variant RCMs and the azimuth-dependent Doppler parameters are studied. First, a reference range linear RCM correction (RCMC) is used to remove the most of the linear RCM components and to mitigate the range-azimuth coupling of the 2-D spectrum. And then, in the azimuth time dimension, a new perturbation function is designed in the extended nonlinear chirp scaling (CS) (ENLCS) algorithm to overcome the azimuth-dependent RCM and to equalize Doppler parameters. To remove both the inherent range-dependent RCM and the linear RCM caused by the range-dependent squint angle, a modified CS (MCS) algorithm with a new scaling function is proposed, and for the residual RCMs, a bulk RCMC and second range compression (SRC) are utilized to compensate them. With the proposed ENLCS and MCS operation, the 2-D spatial-variant RCMC and the azimuth-dependent Doppler equalization are, thus, achieved. The experimental results with simulated data in the case of the high-resolution highly squinted SAR demonstrate the superior performance of the proposed algorithm.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Azimuth Motion Compensation With Improved Subaperture Algorithm for
           Airborne SAR Imaging
    • Authors: Lei Zhang;Guanyong Wang;Zhijun Qiao;Hongxian Wang;
      Pages: 184 - 193
      Abstract: Conventional motion compensation (MOCO) under beam-center approximation is usually insufficient to correct severe track deviations for high-resolution synthetic aperture radar imaging. In this paper, a novel MOCO approach is developed for correction of the azimuth-variant motion errors by exploiting a precise angle-to-Doppler relationship within subapertures. The corruption from the residual motion errors to the angle-to-Doppler mapping is investigated and overcome by a compensation scheme of the scaled Fourier transform. Inheriting the high efficiency, the proposed azimuth MOCO approach has dramatically improved precision over the conventional subaperture MOCO method by reducing high side-lobe peaks of the point spread function. Extensive comparisons with other MOCO algorithms are given to show the superiority of the proposed algorithm. Moreover, real-data experiments are provided for a clear demonstration of our proposed approach.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Estimation of Rice Crop Height From X- and C-Band PolSAR by
           Metamodel-Based Optimization
    • Authors: Onur Yuzugullu;Esra Erten;Irena Hajnsek;
      Pages: 194 - 204
      Abstract: Rice crops are important in global food economy and are monitored by precise agricultural methods, in which crop morphology in high spatial resolution becomes the point of interest. Synthetic aperture radar (SAR) technology is being used for such agricultural purposes. Using polarimetric SAR (PolSAR) data, plant morphology dependent electromagnetic scattering models can be used to approximate the backscattering behaviors of the crops. However, the inversion of such models for the morphology estimation is complex, ill-posed, and computationally expensive. Here, a metamodel-based probabilistic inversion algorithm is proposed to invert the morphology-based scattering model for the crop biophysical parameter mainly focusing on the crop height estimation. The accuracy of the proposed approach is tested with ground measured biophysical parameters on rice fields in two different bands (X and C) and several channel combinations. Results show that in C-band the combination of the HH and VV channels has the highest overall accuracy through the crop growth cycle. Finally, the proposed metamodel-based probabilistic biophysical parameter retrieval algorithm allows estimation of rice crop height using PolSAR data with high accuracy and low computation cost. This research provides a new perspective on the use of PolSAR data in modern precise agriculture studies.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Bridge Displacements Monitoring Using Space-Borne X-Band SAR
           Interferometry
    • Authors: Milan Lazecky;Ivana Hlavacova;Matus Bakon;Joaquim J. Sousa;Daniele Perissin;Gloria Patricio;
      Pages: 205 - 210
      Abstract: The development of interferometric methodologies for deformation monitoring that are able to deal with long time series of synthetic aperture radar (SAR) images made the detection of seasonal effects possible by decomposing the differential SAR phase. In the case of monitoring of man-made structures, particularly bridges, the use of high-resolution X-band SAR data allows the determination of three major components with significant influence on the SAR phase: the linear deformation trend, the height of structures over terrain, and the thermal expansion. In the case of stable metallic or (reinforced) concrete structures, this last effect can reach a magnitude comparable to or even exceeding the other phase components. In this review, we present two case studies that confirm the feasibility of InSAR techniques for bridge deformation monitoring and our original approach to refine the thermal expansion component.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Estimation of Snow Surface Dielectric Constant From Polarimetric SAR Data
    • Authors: Surendar Manickam;Avik Bhattacharya;Gulab Singh;Yoshio Yamaguchi;
      Pages: 211 - 218
      Abstract: A novel methodology is proposed in this paper for the estimation of snow surface dielectric constant from polarimetric SAR (PolSAR) data. The dominant scattering-type magnitude proposed by Touzi et al. is used to characterize scattering mechanism over the snowpack. Two methods have been used to obtain the optimized degree polarization of a partially polarized wave: 1) the Touzi optimum degree of polarization given by Touzi  et al. in 1992. The maximum (p_{\rm \max }) and the minimum (p_{\rm \min}) degree of polarizations are obtained along with the optimum transmitted polarizations (\chi _{t}^{\rm{opt}},\psi _{t}^{\rm{opt}}) . 2) The adaptive generalized unitary transformation-based optimum degree of polarization m_{E}^{\rm{opt}} proposed by Bhattacharya  et al. in 2015. This optimum degree of polarization is obtained either by a real or a complex unitary transformation of the 3 \times 3 coherency matrix. These two degrees of polarizations are used and compared in this study as a criterion to select the maximum number of pixels with surface dominant scattering. These pixels were then used to invert the snow surface dielectric constant. It has been observed that the m_{E}^{\rm{opt}} have increased the number of pixels for inversion by \approx text{9--10}% compared to the original ata. On the other hand, it was observed that the Touzi maximum degree of polarization p_{\rm \max } has increased the number of pixels for inversion by \approx 2% compared to that of m_{E}^{\rm{opt}} . The proposed methodology is applied to Radarsat-2 PolSAR C-band datasets over the Indian Himalayan region. It is observed that the correlation coefficient between the measured and the estimated snow surface dielectric constant is 0.95 at 95% confidence interval with a root mean square error of 0.20.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Integrating Object Boundary in Super-Resolution Land-Cover Mapping
    • Authors: Yuehong Chen;Yong Ge;Yuanxin Jia;
      Pages: 219 - 230
      Abstract: This paper proposes a novel class allocation strategy in units of object (UOO) for soft-then-hard super-resolution mapping (STHSRM). STHSRM involves two processes: 1) subpixel sharpening and 2) class allocation. The UOO is implemented in the second process by integrating the object boundaries as an additional structural constraint. First, UOO obtains the object boundaries from remote-sensing images by image segmentation. The number of subpixels within an object is then calculated for each class to meet the coherence constraint of fractional images imposed by soft classification. Finally, a linear optimization model is built for each object to obtain the optimal hard class labels of subpixels. A synthetic image and two real remote-sensing images are used to evaluate the effectiveness of UOO. The results are compared visually and quantitatively with two existing class allocation methods: 1) the highest attribute values first (HAVF) and 2) units of class (UOC). The experimental results show that UOO performs better than these two methods. UOO can better reduce the salt and pepper effect in resultant maps than both HAVF and UOC when dealing with real remote-sensing images. Moreover, UOO can better maintain the structure of land-cover patches, with smoother boundaries as compared with the two methods.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Color-Based Segmentation of Sky/Cloud Images From Ground-Based Cameras
    • Authors: Soumyabrata Dev;Yee Hui Lee;Stefan Winkler;
      Pages: 231 - 242
      Abstract: Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers) are increasingly used nowadays because of their applications in a number of fields, including climate modeling, weather prediction, renewable energy generation, and satellite communications. Due to the wide variety of cloud types and lighting conditions in such images, accurate and robust segmentation of clouds is challenging. In this paper, we present a supervised segmentation framework for ground-based sky/cloud images based on a systematic analysis of different color spaces and components, using partial least-squares regression. Unlike other state-of-the-art methods, our proposed approach is entirely learning based and does not require any manually defined parameters. In addition, we release the Singapore whole Sky imaging segmentation database, a large database of annotated sky/cloud images, to the research community.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Consequences of Landsat Image Strata Classification Errors on Bias and
           Variance of Inventory Estimates: A Forest Inventory Case Study
    • Authors: Michael K. Crosby;Thomas G. Matney;Emily B. Schultz;David L. Evans;Donald L. Grebner;H. Alexis Londo;John C. Rodgers;Curtis A. Collins;
      Pages: 243 - 251
      Abstract: Use of remotely sensed (e.g., Landsat) imagery for developing sampling frame strata for large-scale inventories of natural resources has potential for increasing sampling efficiency and lowering cost by reducing required sample sizes. Sampling frame errors are inherent with the use of this technology, either from user misclassification or due to flawed technology. Knowledge of these sampling frame errors is important, as they inflate the variance of inventory estimates, particularly poststratified estimates. Forest inventory estimates from the Mississippi Institute for Forest Inventory (MIFI) were utilized to study the extent to which Geographic Information System classification errors (sampling frame errors) affect forest volume and area mean and variance estimates. MIFI's high sampling intensity provided a unique opportunity to quantify the magnitude that different levels of misclassification ultimately have on mean and variance estimates. A variance calculator was developed to assess the impact of various levels of misclassification on least and most variable summary estimates of cubic meter volume percent and total area. The standard error estimates for mean and total volume decreased when plots were reallocated to their correct strata. The increased efficiency obtained from correcting misclassifications illustrates that the loss in precision due to misclassifying inventory strata is consequential. Knowledge and correction of these errors provides a natural-resource-based professional or investor using land classification/inventory data the best minimum risk information possible. A complete set of variance estimators for poststratified means and total area estimates with sampling frame errors are presented and compared to estimators without sampling frame errors.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Atmospheric Correction of Landsat-8/OLI Imagery in Turbid Estuarine
           Waters: A Case Study for the Pearl River Estuary
    • Authors: Haibin Ye;Chuqun Chen;Chaoyu Yang;
      Pages: 252 - 261
      Abstract: Several methods have been proposed for atmospheric correction over turbid waters, including near-infrared (NIR) band based or short-wave infrared (SWIR) band-based where the signal in turbid waters can be assumed zero. Here, we adopt a new infrared extrapolation method to extend the existing turbid water atmospheric correction of the operational land imager (OLI) data on Landsat-8 platform. The atmospheric correction uses the extrapolated Rayleigh-corrected reflectance at NIR and SWIR bands to derive the ratios of NIR to SWIR and visible aerosol single-scattering contributions (aerosol epsilon). Taking the Pearl River Estuary as an example, the magnitude and spatial distribution of reflectance from OLI compare well with those of concurrent moderate resolution imaging spectroradiometer /Aqua based on SWIRE atmospheric correction method. The linear regression coefficients between the resampled OLI and Aqua data have demonstrated the proposed atmospheric correction method can provide robust and realistic reflectance. The advantages of the high spatial resolution made the OLI data a good source for applications in coastal and estuarine waters.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Improving Orthorectification of UAV-Based Push-Broom Scanner Imagery Using
           Derived Orthophotos From Frame Cameras
    • Authors: Ayman Habib;Weifeng Xiong;Fangning He;Hsiuhan Lexie Yang;Melba Crawford;
      Pages: 262 - 276
      Abstract: Low-cost unmanned airborne vehicles (UAVs) are emerging as a promising platform for remote-sensing data acquisition to satisfy the needs of wide range of applications. Utilizing UAVs, which are equipped with directly georeferenced RGB-frame cameras and hyperspectral push-broom scanners, for precision agriculture and high-throughput phenotyping is an important application that is gaining significant attention from researchers in the mapping and plant science fields. The advantages of UAVs as mobile-mapping platforms include low cost, ease of storage and deployment, ability to fly lower and collect high-resolution data, and filling an important gap between wheel-based and manned-airborne platforms. However, limited endurance and payload are the main disadvantages of consumer-grade UAVs. These limitations lead to the adoption of low-quality direct georeferencing and imaging systems, which in turn will impact the quality of the delivered products. Thanks to recent advances in sensor calibration and automated triangulation, accurate mapping using low-cost frame imaging systems equipped with consumer-grade georeferencing units is feasible. Unfortunately, the quality of derived geospatial information from push-broom scanners is quite sensitive to the performance of the implemented direct georeferencing unit. This paper presents an approach for improving the orthorectification of hyperspectral push-broom scanner imagery with the help of generated orthophotos from frame cameras using tie point and linear features, while modeling the impact of residual artifacts in the direct georeferencing information. The performance of the proposed approach has been verified through real datasets that have been collected by quadcopter and fixed-wing UAVs over an agricultural field.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Graph Regularized Nonlinear Ridge Regression for Remote Sensing Data
           Analysis
    • Authors: Renlong Hang;Qingshan Liu;Huihui Song;Yubao Sun;Fuping Zhu;Hucheng Pei;
      Pages: 277 - 285
      Abstract: In this paper, a graph regularized nonlinear ridge regression (RR) model is proposed for remote sensing data analysis, including hyper-spectral image classification and atmospheric aerosol retrieval. The RR is an efficient linear regression method, especially in handling cases with a small number of training samples or with correlated features. However, large amounts of unlabeled samples exist in remote sensing data analysis. To sufficiently explore the information in unlabeled samples, we propose a graph regularized RR (GRR) method, where the vertices denote labeled or unlabeled samples and the edges represent the similarities among different samples. A natural assumption is that the predict values of neighboring samples are close to each other. To further address the nonlinearly separable problem in remote sensing data caused by the complex acquisition process as well as the impacts of atmospheric and geometric distortions, we extend the proposed GRR into a kernelized nonlinear regression method, namely KGRR. To evaluate the proposed method, we apply it to both classification and regression tasks and compare with representative methods. The experimental results show that KGRR can achieve favorable performance in terms of predictability and computation time.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Approximate Area-to-Point Regression Kriging for Fast Hyperspectral Image
           Sharpening
    • Authors: Qunming Wang;Wenzhong Shi;Peter M. Atkinson;Qi Wei;
      Pages: 286 - 295
      Abstract: Area-to-point regression kriging (ATPRK) is an advanced image fusion approach in remote sensing In this paper, ATPRK is considered for sharpening hyperspectral images (HSIs), based on the availability of a fine spatial resolution panchromatic or multispectral image. ATPRK can be used straightforwardly to sharpen each coarse hyperspectral band in turn. This scheme, however, is computationally expensive due to the large number of bands in HSIs, and this problem is exacerbated for multiscene or multitemporal analysis. Thus, we extend ATPRK for fast HSI sharpening with a new approach, called approximate ATPRK (AATPRK), which transforms the original HSI to a new feature space and image fusion is performed for only the first few components before back transformation. Experiments on two HSIs show that AATPRK greatly expedites ATPRK, but inherits the advantages of ATPRK, including maintaining a very similar performance in sharpening (both ATPRK and AATPRK can produce more accurate results than seven benchmark methods) and precisely conserving the spectral properties of coarse HSIs.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Recursive Geometric Simplex Growing Analysis for Finding Endmembers in
           Hyperspectral Imagery
    • Authors: Chein-I Chang;Hsiao-Chi Li;Chao-Cheng Wu;Meiping Song;
      Pages: 296 - 308
      Abstract: Simplex growing algorithm (SGA) is an endmember finding algorithm which searches for endmembers one after another by growing simplexes one vertex at a time via maximizing simplex volume (SV). Unfortunately, several issues arise in calculating SV. One is the use of dimensionality reduction (DR) because the dimensionality of a simplex is usually much smaller than data dimensionality. Second, calculating SV requires calculating the determinant of an ill-ranked matrix in which case singular value decomposition (SVD) is generally required to perform DR. Both approaches generally do not produce true SV. Finally, the computing time becomes excessive and numerically instable as the number of endmembers grows. This paper develops a new theory, called geometric simplex growing analysis (GSGA), to resolve these issues. It can be considered as an alternative to SGA from a rather different point of view. More specifically, GSGA looks into the geometric structures of a simplex whose volume can be actually calculated by multiplication of its base and height. As a result, it converts calculating maximal SV to finding maximal orthogonal projection as its maximal height becomes perpendicular to its base. To facilitate GSGA in practical applications, GSGA is further used to extend SGA to recursive geometric simplex growing algorithm (RGSGA) which allows GSGA to be implemented recursively in a similar manner that a Kalman filter does. Consequently, RGSGA can be very easily implemented with significant saving of computing time. Best of all, RGSGA is also shown to be most efficient and effective among all SGA-based variants.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Firefly-Algorithm-Inspired Framework With Band Selection and Extreme
           Learning Machine for Hyperspectral Image Classification
    • Authors: Hongjun Su;Yue Cai;Qian Du;
      Pages: 309 - 320
      Abstract: A firefly algorithm (FA) inspired band selection and optimized extreme learning machine (ELM) for hyperspectral image classification is proposed. In this framework, FA is to select a subset of original bands to reduce the complexity of the ELM network. It is also adapted to optimize the parameters in ELM (i.e., regularization coefficient C, Gaussian kernel σ, and hidden number of neurons L). Due to very low complexity of ELM, its classification accuracy can be used as the objective function of FA during band selection and parameter optimization. In the experiments, two hyperspectral image datasets acquired by HYDICE and HYMAP are used, and the experiment results indicate that the proposed method can offer better performance, compared with particle swarm optimization and other related band selection algorithms.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection
    • Authors: Alp Ertürk;Marian-Daniel Iordache;Antonio Plaza;
      Pages: 321 - 330
      Abstract: The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance. In contrast to traditional change detection methods, unmixing-based change detection has been shown to have the important added benefit of providing subpixel-level information on the nature of the changes, instead of only providing the location of the changes. Recently, sparse unmixing has also been introduced to hyperspectral change detection, resulting in a method that circumvents the drawbacks of regular spectral unmixing approaches. Sparse unmixing-based change detection reveals the changes that occur in a multitemporal series, at subpixel level, and in terms of the library spectra and their sparse abundances, and provides enhanced change detection performance, especially when subpixel-level changes have occurred. However, sparse unmixing is generally an ill-conditioned and time-consuming process, especially as the size of the utilized spectral library increases. In this paper, dictionary pruning is exploited for the first time for hyperspectral change detection using sparse unmixing, in order to alleviate the ill-conditioning of the problem and achieve decreased computation times and enhanced change detection performance. Experimental results on both realistic synthetic and real datasets are used to validate the proposed approach.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Hyperspectral Image Unmixing Based on Fast Kernel Archetypal Analysis
    • Authors: Chunhui Zhao;Genping Zhao;Xiuping Jia;
      Pages: 331 - 346
      Abstract: Restricted by the associated factors to spatial resolution in remote sensing, mixed pixels and relative pure pixels may both exist in hyperspectral images. In this paper, Kernel Archetypal Analysis (KAA) is investigated for flexible endmember extraction which implicitly takes the intraclass variability into account in relative pure pixel mapping and mixed pixel unmixing. As kernel matrix in KAA brings high computational cost, fast KAA (FKAA) is proposed in this study to relieve KAA's memory issue and reduce KAA's processing time using the Nyström method. Nyström method is used to realize low-rank approximation of the high-dimensional kernel matrix in KAA by using a small portion of informative samples obtained by K-means. Experiments were conducted on both synthetic and real hyperspectral images. The results show that both KAA and FKAA can generate representative endmembers from the mixed data. With proper parameter setting, they can address the intraclass endmember variability in endmember extraction and achieve more realistic unmixing results than conventional geometric methods. In particular, FKAA is able to speed up KAA without significant reduction in unmixing accuracy.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Superpixel-Based Active Learning and Online Feature Importance Learning
           for Hyperspectral Image Analysis
    • Authors: Jielian Guo;Xiong Zhou;Jun Li;Antonio Plaza;Saurabh Prasad;
      Pages: 347 - 359
      Abstract: The rapid development of multichannel optical imaging sensors has led to increased utilization of hyperspectral data for remote sensing. For classification of hyperspectral data, an informative training set is necessary for ensuring robust performance. However, in remote sensing and other image analysis applications, labeled samples are often difficult, expensive, and time-consuming to obtain. This makes active learning (AL) an important part of an image analysis framework—AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task. This paper proposes an AL framework that leverages from superpixels. A spatial-spectral AL method is proposed that integrates spatial and spectral features extracted from superpixels in an AL framework. The experiments with an urban land cover classification and a wetland vegetation mapping task show that the proposed method has faster convergence and superior performance as compared to state of the art approaches. Additionally, our proposed framework has a key additional benefit in that it is able to identify and quantify feature importance—the resulting insights can be highly valuable to various remote sensing image analysis tasks.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • LiDAR Data Filtering and DTM Generation Using Empirical Mode Decomposition
    • Authors: Abdullah H. Özcan;Cem Ünsalan;
      Pages: 360 - 371
      Abstract: LiDAR technology is advancing. As a result, researchers can benefit from high-resolution height data from Earth’s surface. Digital terrain model (DTM) generation and point classification (filtering) are two important problems for LiDAR data. These are connected problems since solving one helps solving the other. Manual classification of LiDAR point data could be time consuming and prone to errors. Hence, it would not be feasible. Therefore, researchers proposed several methods to solve DTM generation and point classification problems. Although these methods work fairly well in most cases, they may not be effective for all scenarios. To contribute in this research topic, a novel method based on two-dimensional (2-D) empirical mode decomposition (EMD) is proposed in this study. Local, nonlinear, and nonstationary characteristics of EMD allow better DTM generation. The proposed method is tested on two publicly available LiDAR dataset, and promising results are obtained. Besides, the proposed method is compared with other methods in the literature. Comparison results indicate that the proposed method has certain advantages in terms of performance.
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Spatial Modeling of Lidar-Derived Woody Biomass Estimates Collected Along
           Transects in a Heterogeneous Savanna Landscape
    • Authors: David Gwenzi;Michael Andrew Lefsky;
      Pages: 372 - 384
      Abstract: Waveforms from the Ice, Cloud and land Elevation Satellite have successfully estimated footprint-level canopy height and aboveground biomass even in structurally complex savanna ecosystems. However, at the landscape level wall-to-wall maps are preferred since they are more easily integrated with other geospatial data products. We evaluated and compared the utility of inverse distance weighting, cokriging, regression kriging and image segmentation methods to create wall-to-wall maps from footprint-level estimates of biomass across a 13 600-Ha Oak savanna landscape in Santa Clara county, California. The four methods estimated biomass with between 39% (inverse distance weighting) and 66% (image segmentation) of variance explained and RMSE of 42% and 32% of the mean, respectively. When more waveforms were available across or along track to characterize patch biomass with the image segmentation method, 78% of variance in biomass was explained (RMSE = 21% of the mean). Overall, the mean biomass estimated by the four methods did not differ significantly but a visual inspection of the output maps showed marked differences in the ability of each model to mimic the primary variable's landscape-level trend. We conclude that transects of lidar data can be used to create wall-to-wall biomass maps in savannas but the methods require a higher sampling intensity and informative auxiliary data to reproduce the variability of the biomass across the landscape. We recommend that future satellite lidar missions increase the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary
      PubDate: Jan. 2017
      Issue No: Vol. 10, No. 1 (2017)
       
 
 
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