Wrapped Unsupervised Hyperspectral Band Selection via Reconstruction Error from Wasserstein Generative Adversarial Network

Wrapped unsupervised band selection (WUBS) is a powerful means of reducing the dimensions of hyperspectral images (HSIs) and has drawn much focus recently. Nevertheless, numerous WUBS approaches struggle to strike a balance between computational complexity and performance and typically disregard high-level information between bands. This paper presents a new reconstruction error-based algorithm called distance density (DD) and Wasserstein generative adversarial network (WGAN)-driven WUBS (DW-WUBS), which is intended to overcome these problems. Minutely, DW-WUBS employs DD to weigh the spectral fluctuation in different band groups and thus determine the detailed expression of the importance of each group. At the same time, it uses a sequential search method on the important band group instead of the original HSIs, thereby reducing the computational complexity of band retrieval. Afterwards, DW-WUBS trains a WGAN and applies its critical network to test the representativeness of the searched bands by considering their contribution to HSI reconstruction. This automatically derives underlying and higher-level structure information of the spectrum. The superiority of DW-WUBS is certified by comprehensive experiments on three benchmark data sets. For instance, on the Pavia Center scene, the peaked mean accuracy (MA) using the twelve bands chosen via DW-WUBS with the CART classifier exceeds the baseline (i.e., all bands) by 0.91% in the classification task.