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- W3148729954 abstract "This paper presents Synthetic Aperture Radar (SAR) patch categorization using unsupervised feature learning framework. It is based on layer based sparse coding, which extends a sparse coding to a multi layer architecture. A contribution of this paper is a framework which consists of 3 layers of sparse coding, local spatial pooling layer, normalization layer, map reduction layer and a classification layer. The new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. The proposed method achieved promising results in SAR patch categorization." @default.
- W3148729954 created "2021-04-13" @default.
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- W3148729954 date "2018-07-01" @default.
- W3148729954 modified "2023-09-23" @default.
- W3148729954 title "SAR Patch Categorization Using Stacked Sparse Coding" @default.
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