Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

Wheat variety identification from hyperspectral images holds significant importance in both
fine breeding and intelligent agriculture. However, the discriminatory accuracy of some …

Deep learning based change detection has brought a significant improvement in the
accuracy and efficiency when compared with conventional machine learning methods …

Recently, the deep learning algorithms have been increasingly utilized in remote sensing
change detection. However, incomplete buildings and the blurred edges caused by the …

Recently, deep learning-based hyperspectral image (HSI) classification techniques are
flourishing and exhibit good performance, where cross-domain information is usually utilized …

Mountain roads are a source of important basic geographic data used in various fields. The
automatic extraction of road images through high-resolution remote sensing imagery using …

Deep learning models have shown great potential in remote sensing (RS) image processing
and analysis. Nevertheless, there are insufficient labeled samples to train deep networks …

Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral
image classification for its excellent feature representation and convolution on arbitrarily …