Hyperspectral image classification has been a vibrant area of research in recent years.
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …

A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …

Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …

The rapid development of high spatial resolution (HSR) remote sensing imagery techniques
not only provide a considerable amount of datasets for scene classification tasks but also …

With the rapid development of the satellite sensor technology, high spatial resolution remote
sensing (HSR) data have attracted extensive attention in military and civilian applications. In …

Hyperspectral image superresolution is a highly attractive topic in computer vision and has
attracted many researchers' attention. However, nearly all the existing methods assume that …

The total variation (TV) is a powerful regularization term encoding the local smoothness prior
structure underlying images. By combining the TV regularization term with low rank prior, the …

To better understand scene images in the field of remote sensing, multi-label annotation of
scene images is necessary. Moreover, to enhance the performance of deep learning models …

Road detection and centerline extraction from very high-resolution (VHR) remote sensing
imagery are of great significance in various practical applications. Road detection and …

Recent low-rank based matrix/tensor recovery methods have been widely explored in
multispectral images (MSI) denoising. These methods, however, ignore the difference of the …