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Band Selection for HSI Classification Using Binary Constrained Optimization
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AI
DOI：10.1109/LGRS.2021.3119346.png)
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Abstract
En 中文
Hyperspectral images (HSIs) containing tens to hundreds of bands can be used in various image classification tasks. However, due to the high data redundancy of the spectral information, the acquiring and analysis of HSIs are usually relatively time-consuming and wasteful of storage space, and therefore limit the practical application of HSIs. Selecting a subset of bands without sacrificing classification accuracy is a strategy to relieve such problems. In this letter, we present an optimization-based method, which can jointly optimize the band selection (BS) and the classification network parameters for HSIs. The proposed method regards the discrete selection problem as a continuous constrained optimization problem and adaptively selects the informative band subsets for classification. Besides, the experimental results on three public datasets show that our BS method outperforms the state-of-the-art methods in terms of classification accuracy.
Keywords:
Optimization
Hyperspectral imaging
Task analysis
Training
Data visualization
Training data
Geoscience and remote sensing
Band selection (BS)
deep neural network
hyperspectral images (HSIs)
optimization algorithm
Journal
IF:
16.4
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Citations:
5.1K
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