Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

The scene classification of high spatial resolution (HSR) imagery involves labeling an HSR
image with a specific high-level semantic class according to the composition of the semantic …

Due to the lack of high-quality labeled data and poor generalization ability of supervised
models in remote sensing scene classification, cross-domain scene classification is …

Extracting buildings from remote sensing (RS) images is an important task with a variety of
applications. Considerable attention has focused on achieving new state-of-the-art (SOTA) …

Crop type mapping is regarded as an essential part of effective agricultural management.
Automated crop type mapping using remote sensing images is preferred for the consistent …

Recent advances in deep learning have dramatically improved the performance of content-
based remote sensing image retrieval (CBRSIR) with the same distribution of training set …

With the development of deep learning, great progress has been made in object detection of
remote sensing (RS) imagery. However, the object detector is hard to generalize well from …

In recent years, convolutional neural networks (CNNs) have made significant progress in
remote sensing scene classification (RSSC) tasks. Because obtaining a large number of …

High spatial resolution (HSR) remote-sensing image scene classification is a crucial way for
land-use interpretation. However, most of the current scene classification methods assume …