LPCN: Lightweight Precise Classification Network for Hyperspectral Remote Sensing Imagery Based on Multiobjective Optimization

Hyperspectral remote sensing image (HSI) has the unique advantages of spectral continuity as well as synchronous acquisition of both image and spectra of objects, which can achieve precise classification. For HSI classification, the deep learning (DL) methods have been fully developed due to its powerful layer-by-layer nonlinear feature learning ability, which is currently the mainstream method. However, current classification networks often refer directly to the fixed framework of natural image processing and usually only focus on accuracy as an optimization goal, resulting in poor HSI data adaptation capability and parameter redundancy. In addition, HSIs usually have dozens of types of ground objects, with similar and mixed spectra, and the phenomenon of overlapping clusters is aggravated, making it difficult to distinguish similar objects. In this article, a lightweight precise classification network (LPCN) for HSI based on multiobjective optimization was proposed. In LPCN, traditional fixed architectures are avoided, hierarchical lightweight search spaces are designed, and spectral attention mechanisms for similar objects are incorporated to improve separability. Moreover, the accuracy and parameter function of the classification network are independently modeled and optimized simultaneously for reducing the amount of network parameters. The effectiveness of LPCN is proved by experiments with three HSI datasets, with 16, 16, and 22 types of ground objects.