Spectral Shift Mitigation for Cross-Scene Hyperspectral Imagery Classification
Huan Liu, Wei Li, Xiang‐Gen Xia, Mengmeng Zhang, Chenzhong Gao, Ran Tao
Abstract
In cross-scene hyperspectral imagery (HSI) classification, labeled samples are only available in source scene, and how to properly reduce the spectral shift between source and target scenes is a matter of concern. In this article, we investigate this issue by considering the causes of the spectral shift and propose spectral shift mitigation (SSM) that includes amplitude shift mitigation (ASM) and adjacency effect mitigation (AEM). First, in ASM, the amplitude shift between source and target scenes is reduced by employing amplitude normalization on pixels of both source and target scenes. Then, in AEM, the spectral variation of target scene caused by adjacency effect is reduced by taking the weighted average spectral vector of surrounding pixels of a query pixel as the new spectral vector of the query pixel. Finally, a classifier trained by labeled samples from source scene is used for target scene. Superior classification performance on several cross-scene HSI data pairs demonstrates the effectiveness of the proposed SSM.