Attention Residual Hybrid Network for Unmanned Aerial Vehicles Hyperspectral Image Classification

作者：Zhen Zhang, Linhuan Jiang, Bo‐Hui Tang, Jianchen Liu, Qingwang Wang, Yabin Hu, Liang Huang, Zhitao Fu · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2025 · DOI：10.1109/jstars.2025.3540910 · 被引用次数：8 · 研究领域：Infrared Target Detection Methodologies、Advanced Measurement and Detection Methods、Remote-Sensing Image Classification

Unmanned aerial vehicle (UAV) hyperspectral images are endowed with abundant spectral information and spatial texture details, which are crucial for the precise classification and monitoring of terrestrial features. Despite the imagery offers high spatial and spectral resolution along with exceptional mobility, UAV-borne hyperspectral images exhibit intricate intraclass spectral variability and high spatial heterogeneity among features, which consequently poses a significant obstacle to the accurate classification. Simultaneously, the high spectral resolution of UAV-borne hyperspectral images, constrained by sensor payload, often results in a relatively low signal-to-noise ratio, posing challenges for traditional deep-learning models in effectively extracting features for fine classification tasks. To address the obstacles presented, this article innovatively proposes a multifeature fusion attention residual hybrid model for UAV-borne hyperspectral imagery classification. The proposed network initially employs an innovative sequential mechanism that leverages multiscale filters and attention residuals, and followed by the fusion of a multicascade 2-D–3-D parallel structure to achieve a semantic–spectral–spatial multifeature fusion, aimed at enhancing the fine classification of UAV-borne hyperspectral imagery. Experimental results using HySpex data highlight the superiority of the proposed approach, achieving an overall accuracy of 80.40%, which represents a significant averag...