Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

Road extraction is to automatically label the pixels of roads in satellite imagery with specific
semantic categories based on the extraction of the topographical meaningful features. For …

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …

Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …

Few-shot scene classification aims to recognize unseen scene concepts from few labeled
samples. However, most existing works are generally inclined to learn metalearners or …

Remote sensing (RS) image scene classification is an important research topic in the RS
community, which aims to assign the semantics to the land covers. Recently, due to the …

This paper proposes a novel end-to-end learning model, called skip-connected covariance
(SCCov) network, for remote sensing scene classification (RSSC). The innovative …

The application of convolutional neural networks has been shown to significantly improve
the accuracy of building extraction from very high-resolution (VHR) remote sensing images …

Accurate urban land-use maps, which reflect the complicated land-use pattern implied in the
function and distribution of land-cover types, play an important role in urban analysis. In …

Few-shot remote sensing scene classification (FSRSSC) is an important task, which aims to
recognize novel scene classes with few examples. Recently, several studies attempt to …