93
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references127

          • Record: found
          • Abstract: not found
          • Article: not found

          Extreme learning machine: Theory and applications

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Stereo processing by semiglobal matching and mutual information.

            This paper describes the Semi-Global Matching (SGM) stereo method. It uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement and multi-baseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed.A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2s on typical test images. An in depth evaluation of the Mutual Information based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Deep Learning-Based Classification of Hyperspectral Data

                Bookmark

                Author and article information

                Journal
                IEEE Geoscience and Remote Sensing Magazine
                IEEE Geosci. Remote Sens. Mag.
                Institute of Electrical and Electronics Engineers (IEEE)
                2168-6831
                2473-2397
                December 2017
                December 2017
                : 5
                : 4
                : 8-36
                Article
                10.1109/MGRS.2017.2762307
                bfa5d362-3011-430b-a687-fac4f6d19777
                © 2017
                History

                Comments

                Comment on this article