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  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 401 - 277 of 277 Journals sorted alphabetically
Revista de Geografia (Recife)     Open Access  
Revista de Geografia e Ordenamento do Território     Open Access  
Revista de Geografía Norte Grande     Open Access   (Followers: 1)
Revista de la Asociacion Geologica Argentina     Open Access  
Revista de Teledetección     Open Access  
Revista del Museo de La Plata     Open Access  
Revista do Instituto de Estudos Brasileiros     Open Access  
Revista Eletrônica : Tempo - Técnica - Território / Eletronic Magazine : Time - Technique - Territory     Open Access  
Revista Espinhaço     Open Access  
Revista Estudios Hemisféricos y Polares     Open Access  
Revista Geama     Open Access  
Revista Geoaraguaia     Open Access  
Revista Geográfica de América Central     Open Access  
Revista Geonorte     Open Access  
Revista Interamericana de Ambiente y Turismo     Open Access  
Revista Intercontinental de Gestão Desportiva     Open Access  
Revista Interdisciplinar da Mobilidade Humana     Open Access  
Revista Latinoamericana de Antropología del Trabajo     Open Access  
Revista Tamoios     Open Access  
Revista Tocantinense de Geografia     Open Access  
Revista Universitaria de Geografía     Open Access  
Revista Uruguaya de Antropología y Etnografía     Open Access  
Revue archéologique du Centre de la France     Open Access   (Followers: 1)
Revue de géographie historique     Open Access   (Followers: 1)
RIEM : Revista Internacional de Estudios Migratorios     Open Access  
Rocznik Toruński     Open Access  
Rural & Urbano     Open Access  
San Francisco Estuary and Watershed Science     Open Access  
Sasdaya : Gadjah Mada Journal of Humanities     Open Access  
Saúde e Meio Ambiente : Revista Interdisciplinar     Open Access  
Scandinavistica Vilnensis     Open Access  
Scientific Annals of Stefan cel Mare University of Suceava. Geography Series     Open Access  
Scottish Geographical Journal     Hybrid Journal   (Followers: 4)
Scripta Nova : Revista Electrónica de Geografía y Ciencias Sociales     Open Access  
Sémata : Ciencias Sociais e Humanidades     Full-text available via subscription  
Seoul Journal of Korean Studies     Full-text available via subscription   (Followers: 4)
Singapore Journal of Tropical Geography     Hybrid Journal   (Followers: 6)
Social Dynamics: A journal of African studies     Hybrid Journal   (Followers: 3)
Social Geography Discussions (SGD)     Open Access   (Followers: 7)
Sociedade & Natureza     Open Access  
South African Geographical Journal     Hybrid Journal   (Followers: 1)
South African Journal of Geomatics     Open Access   (Followers: 2)
South Asian Diaspora     Hybrid Journal   (Followers: 3)
South Australian Geographical Journal     Open Access  
Southeastern Europe     Hybrid Journal   (Followers: 3)
Southeastern Geographer     Full-text available via subscription   (Followers: 2)
Southern African Journal of Environmental Education     Open Access  
Sport i Turystyka : Środkowoeuropejskie Czasopismo Naukowe     Open Access  
Sriwijaya Journal of Environment     Open Access  
Standort - Zeitschrift für angewandte Geographie     Hybrid Journal   (Followers: 3)
Studia Universitatis Babes-Bolyai, Geologia     Open Access  
Studies in African Languages and Cultures     Open Access   (Followers: 1)
Technology and Technique of Typography     Open Access  
Tectonics     Full-text available via subscription   (Followers: 15)
Terra     Open Access  
Terra Brasilis     Open Access  
Terrae Incognitae     Hybrid Journal   (Followers: 1)
Territoire en Mouvement     Open Access  
The Canadian Geographer/le Geographe Canadien     Hybrid Journal   (Followers: 8)
The Geographic Base     Open Access   (Followers: 7)
The Geographical Journal     Hybrid Journal   (Followers: 17)
The South Asianist     Open Access   (Followers: 2)
Third Pole: Journal of Geography Education     Open Access  
Tidsskrift for Kortlægning og Arealforvaltning     Open Access  
Tiempo y Espacio     Open Access  
TRaNS : Trans-Regional-and-National Studies of Southeast Asia     Full-text available via subscription   (Followers: 4)
Transactions of the Institute of British Geographers     Hybrid Journal   (Followers: 28)
Transmodernity : Journal of Peripheral Cultural Production of the Luso-Hispanic World     Open Access   (Followers: 4)
Treballs de la Societat Catalana de Geografia     Open Access  
TRIM. Tordesillas : Revista de investigación multidisciplinar     Open Access  
Turystyka Kulturowa     Open Access  
UD y la Geomática     Open Access  
UNM Geographic Journal     Open Access   (Followers: 1)
Urban Climate     Hybrid Journal   (Followers: 4)
Urban Geography     Hybrid Journal   (Followers: 36)
Urban History Review / Revue d'histoire urbaine     Full-text available via subscription   (Followers: 7)
Urban Research & Practice     Hybrid Journal   (Followers: 19)
Vegueta : Anuario de la Facultad de Geografía e Historia     Open Access  
Visión Antataura     Open Access   (Followers: 5)
Water International     Hybrid Journal   (Followers: 19)
Watershed Ecology and the Environment     Open Access  
Wellbeing, Space & Society     Open Access   (Followers: 4)
Yearbook of the Association of Pacific Coast Geographers     Full-text available via subscription   (Followers: 2)
Załącznik Kulturoznawczy / Cultural Studies Appendix     Open Access   (Followers: 1)

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Human Geography Journal
Number of Followers: 4  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2076-1333 - ISSN (Online) 2312-1130
Published by V.N. Karazin Kharkiv National University Homepage  [7 journals]
  • Impact of water vapour on polymer classification using in situ short-wave
           infrared hyperspectral imaging

    • Authors: Muhammad Saad Shaikh, Benny Thörnberg
      Pages: 1 - 12
      Abstract: Hyperspectral remote sensing is known to suffer from wavelength bands blocked by atmospheric gases. Short-wave infrared hyperspectral imaging at in situ installations is shown to be affected by water vapour even if the pathlength of light through air is only hundreds of centimetres. This impact is especially noticeable with large variations of relative humidity, the coefficient of variation reaching 5 % in our test case. Using repeated calibrations of imaging system at the same relative humidity as in the measurement, we were able to reduce the coefficient of variation to 1 %. The measurement variations are also shown to induce significant error in material classification. Polymer type identification was selected as the test case for material classification. The measurement variations due to the change in relative humidity are shown to result in 20 % classification error at its minimum. With repeated calibrations or by eliminating the most affected wavelength bands from measurements, we were able to reduce the classification error to less than 1 %. Such improvement of measurement and classification precision may be important for industrial applications such as waste sorting, polymer classification etc.
      Citation: J. Spectral Imaging 11, a5 (2022)
      PubDate: 2022-06-01
      DOI: 10.1255/jsi.2022.a5
      Issue No: Vol. 11 (2022)
  • Hyperspectral image non-linear unmixing using joint extrinsic and
           intrinsic priors with L1/2-norms to non-negative matrix factorisation

    • Authors: K. Priya, K. K. Rajkumar
      Pages: 1 - 19
      Abstract: Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance. HU enhances the quality of both spectral and spatial dimensions of the image by modifying the endmember and abundance parameters of the hyperspectral images. There are several HU algorithms available in the literature based on the linear mixing model (LMM) that deals with the microscopic contents of the pixels in the images. Non-negative matrix factorisation (NMF) is the prominent method widely used in LMMs that simultaneously estimates both the endmembers and abundances parameters along with some residual factors of the image to improve the quality of unmixing. In addition to this, the quality of the image is enhanced by incorporating some constraints to both endmember and abundance matrices with the NMF method. However, all the existing methods apply any of these constraints to the endmember and abundance matrices by considering the linearity features of the images. In this paper, we propose an unmixing model called joint extrinsic and intrinsic priors with L1/2 norms to non-negative matrix factorisation (JEIp L1/2-NMF) that applies multiple constraints simultaneously to both endmember and abundance matrices of the hyperspectral image to enhance its quality. Three main external and internal constraints such as minimum volume, sparsity and total variation are applied to both the endmembers and abundance parameters of the image. In addition, a L1/2-norms is imposed to extract good quality spectral data. Therefore, the proposed method enhances spatial as well as spectral data and considers the non-linearity of the pixels in the image by adding a residual term to the model. Performance of our proposed model is measured by using different quality measuring indexes on four benchmark public datasets and found that the proposed method shows outstanding performance compared to all the conventional baseline methods. Further, we also evaluated the performance of our method by varying the number of endmembers empirically and concluded that less than five endmembers provides high-quality spectral and spatial data during the unmixing process.
      Citation: J. Spectral Imaging 11, a4 (2022)
      PubDate: 2022-04-07
      DOI: 10.1255/jsi.2022.a4
      Issue No: Vol. 11 (2022)
  • Data processing of three-dimensional vibrational spectroscopic chemical
           images for pharmaceutical applications

    • Authors: Hannah Carruthers, Don Clark, Fiona C. Clarke, Karen Faulds, Duncan Graham
      Pages: 1 - 8
      Abstract: Vibrational spectroscopic chemical imaging is a powerful tool in the pharmaceutical industry to assess the spatial distribution of components within pharmaceutical samples. Recently, the combination of vibrational spectroscopic chemical mapping with serial sectioning has provided a means to visualise the three-dimensional (3D) structure of a tablet matrix. There are recognised knowledge gaps in current tablet manufacturing processes, particularly regarding the size, shape and distribution of components within the final drug product. The performance of pharmaceutical tablets is known to be primarily influenced by the physical and chemical properties of the formulation. Here, we describe the data processing methods required to extract quantitative domain size and spatial distribution statistics from 3D vibrational spectroscopic chemical images. This provides a means to quantitatively describe the microstructure of a tablet matrix and is a powerful tool to overcome knowledge gaps in current tablet manufacturing processes, optimising formulation development.
      Citation: J. Spectral Imaging 11, a3 (2022)
      PubDate: 2022-03-30
      DOI: 10.1255/jsi.2022.a3
      Issue No: Vol. 11 (2022)
  • A semi-supervised cycle-GAN neural network for hyperspectral image
           classification with minimum noise fraction

    • Authors: Tatireddy Subba Reddy, Jonnadula Harikiran
      Pages: 1 - 14
      Abstract: Hyperspectral imaging (HSI) is a popular mode of remote sensing imaging that collects data beyond the visible spectrum. Many classification techniques have been developed in recent years, since classification is the most crucial task in hyperspectral image processing. Furthermore, extracting features from hyperspectral images is challenging in many scenarios. The semi-supervised classification of HSI is motivated by the Cycle-GAN method that has been proposed in this research paper. Since the proposed HSI classification method is semi-supervised, it makes extensive use of the labelled samples, which are short and have numerous unlabelled images. The research is carried out in two phases. First, to extract the spectral–spatial features, the minimum noise fraction is adopted. And, second, the classification of the semi-supervised method is done by the cycle-GANs. Subsequently, the proposed architecture is implemented on three standard hyperspectral dataset methods. As a result, the performance comparison is carried out in the same field as state-of-the-art approaches. The obtained results successfully demonstrate the supremacy of the proposed technique in the classification of HSI.
      Citation: J. Spectral Imaging 11, a2 (2022)
      PubDate: 2022-03-29
      DOI: 10.1255/jsi.2022.a2
      Issue No: Vol. 11 (2022)
  • An outlook: machine learning in hyperspectral image classification and
           dimensionality reduction techniques

    • Authors: Jonnadula Harikiran, Tatireddy Subba Reddy
      Pages: 1 - 17
      Abstract: Hyperspectral imaging is used in a wide range of applications. When used in remote sensing, satellites and aircraft are employed to collect the images, which are used in agriculture, environmental monitoring, urban planning and defence. The exact classification of ground features in the images is a significant research issue and is currently receiving greater attention. Moreover, these images have a large spectral dimensionality, which adds computational complexity and affects classification precision. To handle these issues, dimensionality reduction is an essential step that improves the performance of classifiers. In the classification process, several strategies have produced good classification results. Of these, machine learning techniques are the most powerful approaches. As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. Moreover, this paper shows the effectiveness of all these techniques for hyperspectral image classification and dimensionality reduction. Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.
      Citation: J. Spectral Imaging 11, a1 (2022)
      PubDate: 2022-01-07
      DOI: 10.1255/jsi.2022.a1
      Issue No: Vol. 11 (2022)
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