Heterogeneous Cuckoo Search-Based Unsupervised Band Selection for Hyperspectral Image Classification

Hyperspectral image (HSI) characteristics of the abundant spectral information are favored by many scholars, but the challenge is how to select relevant features from such high-dimensional data. Band selection (BS), one of the most fundamental dimensionality reduction (DR) techniques, removes redundant bands while providing a subset of bands that can preserve high information content and low noise for further HSI classification. Cuckoo search (CS) algorithm is well known for its high performance of searching relevant features but struggles to get rid of local extremes in the late iteration. Therefore, in this article, an unsupervised BS method based on the heterogeneous CS algorithm with matched filter (HCS-MF) is proposed for HSI classification, in which an optimization model is constructed based on the sensitivity of the matching filter to noise. To reduce the similarity between selected bands, a mapping method based on neighborhood band grouping (NBG) is proposed. In addition, an automatic recommendation strategy based on sliding spectrum decomposition (SSD) is proposed to determine the minimum recommended number of selected bands in different scenes. The superiority of the selected subset of bands is verified by random forest, support vector machine (SVM), and edge-preserving filtering-based SVM (EPF-SVM) classifiers. Experimental results on three well-known datasets demonstrate the robustness and superiority of the proposed HCS-MF algorithm compared with the state-of-the-art methods, such as marginalized graph self-representation (MGSR), neighborhood grouping normalized matched filter (NGNMF), and region-wise multiple graph fusion (RMGF).