In remote sensing (RS), multiple modalities of data are usually available, eg, RGB,
multispectral, hyperspectral, light detection and ranging (LiDAR), and synthetic aperture …

Deep learning has been widely used in automatic target recognition (ATR) for synthetic
aperture radar (SAR) recently. However, most of the studies are based on the network …

Multimodal learning has shown great potentials in numerous scenes and attracts increasing
interest recently. However, it often encounters the problem of missing modality data and thus …

With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

Learning-based multimodal data has attracted increasing interest in the remote sensing
community owing to its robust performance. Although it is preferable to collect multiple …

Multimodal learning robust to missing modality has attracted increasing attention due to its
practicality. Existing methods tend to address it by learning a common subspace …

Recent research on the joint classification of multimodal remote sensing data has achieved
great success. However, due to the limitations imposed by imaging conditions, the case of …

Multimodal land cover classification (MLCC) is a fundamental problem in remote sensing
interpretation, which can obtain excellent performance on account of the complementary …

Taking advantage of multi-modal radiology-pathology data with complementary clinical
information for cancer grading is helpful for doctors to improve diagnosis efficiency and …

In recent times, notable advancements have been achieved in amalgamating
heterogeneous remote sensing imagery to facilitate Earth observation through the adoption …