Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 543-548, 2014
https://doi.org/10.5194/isprsarchives-XL-8-543-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 543-548, 2014
https://doi.org/10.5194/isprsarchives-XL-8-543-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Nov 2014

28 Nov 2014

Enhancement of snow cover change detection with sparse representation and dictionary learning

D. Varade and O. Dikshit D. Varade and O. Dikshit
  • Department of Civil Engineering, IIT Kanpur, 208016, Kanpur, India

Keywords: Snow Cover Change Detection, NDSI, Sparse Representation, K-SVD, k-means clustering

Abstract. Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.