Artificial gorilla troops optimizer enfolded broad learning system for spatial-spectral hyperspectral image classification

Abstract

The key challenge of hyperspectral image (HSI) classification is to develop effective feature representations for reducing intra-class variations while expanding inter-class differences. It is of great interest to exploit texture information in the preprocess stage for HSI classification by using feature extraction operators. Additionally, deep learning (DL) networks have focused on spatial-spectral information extraction. Nevertheless, DL-based approaches are time-consuming to compute and difficult to understand owing to the large number of network parameters and insufficient training data. Responding to these issues, this paper introduces a novel spatial-spectral classification framework based on local optimal oriented pattern (LOOP), artificial gorilla troops optimizer (GTO) enfolded broad learning system (BLS), and bi-exponential edge-preserving smoother (LGBB), which mainly includes the following parts: First, the principal component analysis is used to shrink HSI’s spectral dimensions, and then LOOP extracts the rotation-invariant texture-based spatial signatures from the principal components; Next, GTO, a novel nature-inspired and gradient-free optimization algorithm, is first introduced to BLS for finding the global optimum feature subsets, and then LOOP features are fed into the improved BLS to produce soft-classified probability maps; Finally, post-processing filtering is conducted to optimize the soft-classified probability maps by considering spatial contextual information. Experiment results on four actual HSIs demonstrate that the LGBB framework outperforms previous state-of-the-art approaches in quantitative and qualitative outcomes, especially when training samples are constrained.