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International Journal of Remote Sensing Applications
   [9 followers]  Follow    
  This is an Open Access Journal Open Access journal
     ISSN (Print) 2226-4361 - ISSN (Online) 2226-4353
     Published by Science and Engineering Publishing Company Homepage  [47 journals]
  • Error Analyses of the Sea Ice Draft Retrieval from Upward Looking Sonar

    • Abstract: Error Analyses of the Sea Ice Draft Retrieval from Upward Looking Sonar
      Pages 145-151
      Author Vera Djep
      Sea Ice Draft (SID) has been recorded in the last 40 years from Upload looking Sonar (ULS) on submarines, but not all data are error corrected. Error corrected SID with uncertainties is required for validation of climate models, satellite observations and for assessment of the seasonal and annual SID change due to climate impact. The aim of this study is to analyse the uncertainties of the SID, derived from ULS and develop algorithms for error correction and validation of the retrieved SID from ULS. The uncertainties of the retrieved SID (from ULS on submarine) are analysed. Algorithms for error correction of SID with open water offset and beam width impact are developed and applied to correct SID, retrieved from ULS in the Beaufort Sea in 2007. A bias correction function of raw SID data is provided. The comparison of SID from ULS with collocated SID derived from Radar Altimeter (RA2/Envisat) demonstrated improved biases and correlation coefficient, which confirms the accuracy of the bias-corrected SID. Error corrected SID, derived from ULS, has been applied to validate an algorithm for SID retrieval from RA, using variable ice density. Algorithm for retrieval of sea ice density from the sea ice freeboard, derived from RA, is developed and results of retrieved sea ice density in Beaufort Sea are shown. The developed error correction algorithms of SID, retrieved from ULS, have global application for correction of SID, which are not yet error corrected. Error corrected SID and the derived sea ice densities are essential climate variables (ECV) important for improved climate forecast and validation of satellite observations. European Space Agency (ESA), National Snow and Ice Data centre (NSIDC), climate change and numerical prediction programs will benefit the results of this paper.
      PubDate: 2014-12
       
  • Wide-Angle High Resolution SAR Imaging and Robust Automatic Target
           Recognition of Civilian Vehicles

    • Abstract: Wide-Angle High Resolution SAR Imaging and Robust Automatic Target Recognition of Civilian Vehicles
      Pages 152-164
      Author Deoksu LimLuzhou XuYijun SunJian L
      This paper focuses on wide-angle synthetic aperture radar (SAR) imaging and automatic target recognition of civilian vehicles. A recently proposed hybrid data adaptive method is applied to generate accurate and sparse SAR images of civilian vehicles. We combine projection slice theorem (PST) with 2-D FFT to obtain a more accurate pose estimation than the established PST. Given the so-obtained pose estimates, the horizontal and vertical cumulative-sum-vector (CSV) profiles are utilized to focus the SAR image only on the vehicle of current interest. The corresponding vertical CSV is used as a simple feature for automatic target recognition (ATR). We adopt the local learning based feature selection for ATR. The effectiveness of the entire chain of imaging, pose estimation, feature extraction, and ATR methods is verified using the experimentation results based on the publicly available GOTCHA SAR data set. We demonstrate that the high resolution SAR imaging results in much improved ATR performance compared to the conventional SAR imaging.
      PubDate: 2014-12
       
 
 
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