Cross-Scene Hyperspectral Feature Selection via Hybrid Whale Optimization Algorithm With Simulated Annealing

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.2473-2483
• Main Authors: Wang, Jianxi, Ye, Minchao, Xiong, Fengchao, Qian, Yuntao
• Format: Article
• Language: English
• Subjects: Algorithms; Atmospheric conditions; Classification; Cross-domain WOASA; cross-scene feature selection; Dimensions; Domains; Feature extraction; Feature selection; hyperspectral images; Hyperspectral imaging; Land cover; Optimization; Optimization algorithms; Search problems; Simulated annealing; Space exploration; Spectra; Spectral bands; Spirals; Stability; Training; Whales
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3056593

Hyperspectral images (HSIs) include hundreds of spectral bands, which lead to Hughes phenomenon in classification task and decrease the classification accuracy. Feature selection can remove redundant and noisy features in the HSIs to overcome this phenomenon. In real applications, we may face a HSI scene with only a few labeled samples. Meanwhile, there are adequate labeled samples in a similar HSI scene. For example, they share the same land-cover classes. The shared information can be used to help the scene with a few labeled samples in feature selection. Traditional single-scene-based feature selection appears powerless in solving such problems. Cross-scene feature selection provides an attractive way to select feature subsets by simultaneously using the information from two HSI scenes. However, spectral distribution may change due to atmospheric conditions, yielding spectral shift. In order to tackle this problem, we propose a cross-domain algorithm based on hybrid whale optimization algorithm with simulated annealing (WOASA). The newly proposed algorithm is dubbed cross-domain WOASA (CDWOASA). CDWOASA simultaneously considers the separability of different land-cover classes and the consistency of selected features between two scenes, leading to discriminative and domain-invariant characters of selected feature subset. Moreover, since the original WOASA is not able to precisely control the dimension of selected features, we propose an improvement using a sorting strategy based on the fitness function value, thus making the output feature dimension precisely controlled. The experimental results on two cross-scene HSI datasets demonstrate the superiority of CDWOASA in cross-scene feature selection.