# HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing

Source: [HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing](<https://www.sota2.com/research/paper/68fa54a31e209131ce98d365>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T13:06:29.436Z

Authors: Victor Stefan Aldea

Published: 2014-12-08T18:14:04.000Z

## Abstract

Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper a new convex method of hyperspectral image classification is developed based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. To further enhance class separability, the algorithm is kernelized using an RBF kernel and the final results are improved by a combination of spatial pre and post-processing operations. It is shown that the proposed method is competitive with state of the art algorithms such as SVM-CK, KSOMP-CK and KSSP-CK.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1412.2684>)

[PDF](<https://arxiv.org/pdf/1412.2684.pdf>)