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.

Sparse modeling of the land use classification problem

Mohamed Lamine Mekhalfi;
2015

Abstract

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.
978-1-4799-7929-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/331860
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