In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

An accurate identification of objects from the acquisition system depends on the clear
segmentation and classification of remote sensing images. With the limited financial …

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 …

Agricultural greenhouses (AGs) are an important facility for the development of modern
agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning …

Building extraction from high-resolution remote sensing images is of great significance in
urban planning, population statistics, and economic forecast. However, automatic building …

Semantic segmentation is an essential part of deep learning. In recent years, with the
development of remote sensing big data, semantic segmentation has been increasingly …

The past few decades have witnessed great progress for unmanned aerial vehicles (UAVs)
in civilian fields, especially in photogrammetry and remote sensing. In contrast with manned …

Street-level imagery has covered the comprehensive landscape of urban areas. Compared
to satellite imagery, this new source of image data has the advantage in fine-grained …

With the ongoing development of Earth observation techniques, huge amounts of remote
sensing images with a high spectral-spatial-temporal resolution are now available, and have …