Inspired by recent development of artificial satellite, remote sensing images have attracted
extensive attention. Recently, notable progress has been made in scene classification and …

With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …

Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …

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 …

In this article, we propose an efficient and effective framework to fuse hyperspectral and light
detection and ranging (LiDAR) data using two coupled convolutional neural networks …

Advances in multisource remote sensing have allowed for the development of more
comprehensive observation. The adoption of deep convolutional neural networks (CNN) …

Convolutional neural networks (CNNs) have attracted increasing attention in the field of
multimodal cooperation. Recently, the adoption of CNN-based methods has achieved …

Oblique aerial images offer views of both building roofs and façades, and thus have been
recognized as a potential source to detect severe building damages caused by destructive …

In recent years, collaborative classification of multimodal data, eg, hyperspectral image (HSI)
and light detection and ranging (LiDAR), has been widely used to improve remote sensing …

The multisensory fusion of remote sensing data has obtained a great attention in recent
years. In this letter, we propose a new feature fusion framework based on deep neural …