Sparse modeling of the land use classification problem

In this paper, we present a fusion method contextualized within a land use classification framework. At first, feature vectors are extracted from all the color channels of the given test image. Then, the generated vectors are recovered over a bunch of training feature vectors extracted from training images. The resulting reconstruction residuals feed a fusion mechanism to further compose a final residual that serves for inferring the final decision of the class pertaining to the test image. Validated on a benchmark dataset, the presented method shows to promote drastic improvements over using only one single spectral channel. Furthermore, encouraging gains have been recorded with respect to reference works.