The fusion of multisource remote sensing (RS) data has demonstrated significant potential in
target recognition and classification tasks. However, there is limited emphasis on capturing …

Advancements in Earth observation technologies have greatly enhanced the potential of
integrating hyperspectral (HS) images with Light Detection and Ranging (LiDAR) data for …

The spatial resolution of hyperspectral images (HSIs) is usually limited due to internal
imaging mechanisms. To obtain imagery with high spectral and high spatial resolutions …

With the increasing number of remote sensing (RS) data sources, the joint utilization of
multimodal data in Earth observation tasks has become a crucial research topic. As a typical …

Deep learning (DL) plays an increasingly important role in Earth observation by multisource
remote sensing. However, the current DL-based methods do not make a fully use of the …

At present, hyperspectral image classification (HSIC) technology has been warmly
concerned in all walks of life, especially in agriculture. However, existing classification …

The pretrained vision-language models (VLMs) have achieved outstanding performance in
various visual tasks, primarily due to the knowledge they have acquired from massive image …

The fusion of multimodal data plays a crucial role in classification tasks. However, existing
research typically mines and analyzes the individual features of each data source separately …

The joint use of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has
gained significant performance on land-cover classification. Although spatial–spectral …

The rapid development of sensor technology has made multi-modal remote sensing data
valuable for land cover classification due to its diverse and complementary information …