Conditional Gaussian Enhanced Dense Correlation Matching for Cross-Category Land Cover Classification

Huan Ni, Haiyan Guan, Xudong Tong, Jocelyn Chanussot

Abstract: As the requirements for the downstream tasks of land cover classification (LCC) continue to increase, the category system used for LCC is constantly being refined. This causes previous land cover products and manually annotated training samples to become quickly outdated. Meanwhile, manually annotating samples with more refined categories is extremely time-consuming. To address this impasse, a cross-category knowledge transfer process is needed that can directly generate land cover products under a fine-scale category system using existing training samples under a large-scale category system. Accordingly, this article proposes a cross-category LCC method called conditional Gaussian enhanced dense correlation matching (CGE-DCM). CGE-DCM uses samples under a large-scale category system for training. It then uses only one annotated example of each fine-scale category to achieve fine-scale LCC. In cases with very little sample support, the problems caused by different spectra of the same object and different objects of the same spectrum in complex scenes can be particularly severe. To solve this problem and improve the accuracy of classifications of complex objects, CGE-DCM offers a dense correlation matching (DCM) strategy. In addition, context distribution is different under fine-scale category systems than it is under large-scale category systems. For this reason, CGE-DCM features a conditional Gaussian enhancement mechanism and designs different loss functions for degenerated and nondegenerated scenarios to ensure the stability of the model. Extensive experiments on Gaofen-2 and Orbita hyperspectral satellite (OHS) images demonstrate the effectiveness of each module in CGE-DCM and its superiority over existing methods.

External IDs:dblp:journals/tgrs/NiGTC25