A Similarity-Based Ranking Method for Hyperspectral Band Selection
| Autor: | Weijun Hou, Buyun Xu, Yiting Wang, Wei Yiwei, Xihai Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Dimensionality reduction Feature extraction Hyperspectral imaging Pattern recognition Overfitting Ranking (information retrieval) Euclidean distance Similarity (network science) General Earth and Planetary Sciences Artificial intelligence Electrical and Electronic Engineering Cluster analysis business |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 59:9585-9599 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2020.3048138 |
| Popis: | Band selection (BS) is a commonly used dimension reduction technique for hyperspectral images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a density-based clustering algorithm. The representativeness of a band is evaluated according to its ability to become a cluster center. We introduce structural similarity (SSIM) to measure the relationships between the bands. Thus, our proposed ranking-based BS method is called SR-SSIM. We picked state-of-the-art BS methods as competitors and carried out classification experiments on different data sets. The results illustrated that SR-SSIM outperformed the other methods. It is demonstrated, in this article, that the SSIM is more suitable for hyperspectral BS than the Euclidean distance since the SSIM can mine the spatial information contained in the band images. Furthermore, we discuss the application of BS methods on deep learning classifier. We found that proper preprocessing by the BS method can effectively eliminate redundant information and avoid overfitting. |
| Databáze: | OpenAIRE |
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