A Joint Landscape Metric and Error Image Approach to Unsupervised Band Selection for Hyperspectral Image Classification

Band selection has been proven to be effective in reducing the dimensionality of the hyperspectral image by finding the most distinctive and informative bands. An essential operation for band selection is to quantify band similarity using metrics, such as entropy and mutual information. For the first time, we proposed that this quantification can also be conducted by borrowing the core ideas from landscape ecology, namely employing landscape metrics. To validate this proposal, we first developed a joint landscape metric and error image approach to quantify the similarity between two bands. Using the quantified similarity and Boltzmann entropy-based information content, we then proposed an efficient priority-based band selection algorithm to search optimal bands. To evaluate the proposed approach, we carried out a comprehensive evaluation involving 80 possible landscape metrics, two methods for quantifying band similarity, four classifiers, and four state-of-the-art, popular approaches as the benchmark. Extensive experimental results demonstrated that the proposed approach exhibited global superiority over these benchmark approaches using all these classifiers. We also found that the best choices of landscape metrics to implement the proposed approach came from the following two categories of metrics: aggregation and diversity. Although this letter presents the first study of its kind in employing landscape metrics for unsupervised band selection, it indicated that landscape metrics might open a door for metric-based approaches for image processing, including band selection.