Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …

Remote sensing image scene classification with deep learning (DL) is a rapidly growing
field that has gained significant attention in the past few years. While previous review papers …

Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …

Efficiently implementing remote sensing image classification with high spatial resolution
imagery can provide significant value in land use and land cover (LULC) classification. The …

This paper proposes a new method, called multilayer stacked covariance pooling (MSCP),
for remote sensing scene classification. The innovative contribution of the proposed method …

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

This paper presents an effective scene classification approach based on collaborative
representation fusion of local and global spatial features. First, a visual word codebook is …

With the development of deep learning, supervised feature learning methods have achieved
prominent performance in the field of aerial scene classification. However, supervised …

Object detection in high spatial resolution remote sensing images (HSRIs) is an important
part of image information automatic extraction, analysis, and understanding. The region of …

Scene change detection between multitemporal image scenes can be used to interpret the
variation of regional land use, and has significant potential in the application of urban …