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 This causes previous land cover products and manually annotated training samples to become quickly outdated.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 paper 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 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 images demonstrate the effectiveness of each module in CGE-DCM and its superiority over existing methods.

GaoFen (GF) series are the space-based system as part of the China High-Resolution Earth Observation System (CHEOS), which is established as one of the Chinese National Science and Technology Major Project. Three satellites of the series are already launched till now, and providing near-real time Earth observation with high spatial resolution of 1m and high spectral resolution reaching nanometer level. Data acquired from all these satellites has served a wide range of applications covering many topics. In particular, data of GF-1 is applied in monitoring land cover, soil salinization, sea ice, marine oil spill and so on. And GF-2, with high spatial resolution of 1m, has been used to monitor urban changes and survey the coastline. In addition, all users can access all available data through the data service platform of CHEOS, where also all practical applications cases are detailed.