Learning Global Context and Fine Structures for Enhanced Hyperspectral Subpixel Mapping

Wen Zhou, Ailong Ma, Da He, Yanfei Zhong

Abstract: Subpixel mapping (SPM) is a crucial technique in remote sensing imagery analysis, aimed at characterizing subpixel distribution within the mixed pixels. Traditional SPM methods and convolutional neural network (CNN)-based SPM methods primarily rely on local spatial autocorrelation, which limits their ability to capture long-range dependencies between distant locations or objects. To address this limitation, we propose a global-local spatial dependence integrator for the SPM method (GLSDSPM) that employs both CNN and the vision transformer as dual-path structures to model global context and local spatial dependencies efficiently. Besides, the previous SPM methods often struggle to accurately reconstruct high-quality spatial patterns for linear features, such as slender rivers and roads, due to insensitivity to textures and sharp, high-frequency details. To overcome this challenge, we integrate a linear pattern refinement module (LPRM) into GLSDSPM, which adaptively focuses on thin and long local structures to accurately capture high-frequency features and detailed information. Two experiments conducted on the Pavia and Houston hyperspectral images prove that the proposed method achieves superior performance, outperforming the state-of-the-art by 4.13% and 3.95% in overall accuracy (OA), respectively.

External IDs:dblp:journals/lgrs/ZhouMHZ25