Aircraft detection from very high resolution (VHR) remote sensing images has been drawing
increasing interest in recent years due to the successful civil and military applications. …

Semantic segmentation is important for the understanding of subdecimeter aerial images. In
recent years, deep convolutional neural networks (DCNNs) have been used widely for …

Hyperspectral remote sensing images (HSIs) have been applied in urban planning,
environmental monitoring, and other fields. However, they are susceptible to noise interference, …

Thermal infrared (TIR) remote-sensing imagery can allow objects to be imaged clearly at
night through the long-wave infrared, so that the fusion of thermal infrared and visible (VIS) …

As the acquisition of very high resolution (VHR) satellite images becomes easier owing to
technological advancements, ever more stringent requirements are being imposed on …

Deep neural networks (DNNs) face many problems in the very high resolution remote sensing
(VHRRS) per-pixel classification field. Among the problems is the fact that as the depth of …

The complementarity of synthetic aperture radar (SAR) and optical images allows remote
sensing observations to “see” unprecedented discoveries. Image matching plays a …

In this study, we introduce the R package shp2graph, which provides tools to convert a spatial
network into an ‘igraph’ graph of the igraph R package. This conversion greatly empowers …

Using deep learning to improve the capabilities of high-resolution satellite images has
emerged recently as an important topic in automatic classification. Deep networks track …

Regional remote sensing image products are playing an important role in an increasing number
of application fields. Aiming at multi-satellite imaging task planning for large-area image …