Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

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 …

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

Remote sensing image scene classification is one of the most challenging problems in
understanding high-resolution remote sensing images. Deep learning techniques …

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 …

Remote sensing scene classification remains challenging due to the complexity and variety
of scenes. With the development of attention-based methods, Convolutional Neural …

Deep convolutional neural networks have become an indispensable method in remote
sensing image scene classification because of their powerful feature extraction capabilities …

Automatic extraction of building footprints from high-resolution satellite imagery has become
an important and challenging research issue receiving greater attention. Many recent …

Mapping forest types and tree species at regional scales to provide information for
ecologists and forest managers is a new challenge for the remote sensing community. Here …

With the development of earth observation technologies, the acquired remote sensing
images are increasing dramatically, and a new era of big data in remote sensing is coming …