Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model.

ABSTRACT: As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively, particularly for heterogeneous data sources. Moreover, to

SUBMITTER: Hong D 

PROVIDER: S-EPMC8336649 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature