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This paper evaluates several representative algorithms on real datasets. It analyzes Vertex Component Analysis (VCA), Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization (RSNMF), Sparse Hyperspectral Unmixing (HU) with Mixed Norms, and Hapke Model with Convolutional Neural Network (HapkeCNN). Results indicate that VCA achieves high accuracy in endmember extraction and is widely applicable, RSNMF perform well with fewer, distinct endmembers, Sparse HU with Mixed Norms estimates abundances effectively without high-precision endmembers, addressing endmember variability, and HapkeCNN excels in nonlinear fitting and noise resistance, validating the effectiveness of cognitive models in unmixing tasks.

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In-Depth Evaluation and Analysis of Hyperspectral Unmixing Algorithms with Cognitive Models

  • 会议论文

  • 发表日期：

    2025年

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  • Shunan Deng,
  • Jinchang Ren,
  • Rongjun Chen,
  • Huimin Zhao,
  • Amir Hussain

摘要

This paper evaluates several representative algorithms on real datasets. It analyzes Vertex Component Analysis (VCA), Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization (RSNMF), Sparse Hyperspectral Unmixing (HU) with Mixed Norms, and Hapke Model with Convolutional Neural Network (HapkeCNN). Results indicate that VCA achieves high accuracy in endmember extraction and is widely applicable, RSNMF perform well with fewer, distinct endmembers, Sparse HU with Mixed Norms estimates abundances effectively without high-precision endmembers, addressing endmember variability, and HapkeCNN excels in nonlinear fitting and noise resistance, validating the effectiveness of cognitive models in unmixing tasks.