Contextual information plays a pivotal role in the semantic segmentation of remote sensing
imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The …

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing
images (RSIs). However, conventional methods predominantly focus on learning …

Automatically extracting water bodies is a significant task in interpreting remote sensing
images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance …

Water body extraction is a typical task in the semantic segmentation of remote sensing
images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional …

Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has
made significant process in recent years. Nevertheless, their vulnerability to adversarial …

Extracting water bodies is an important task in remote sensing imagery (RSI) interpretation.
Deep convolution neural networks (DCNNs) show great potential in feature learning; they …

Semantic segmentation of high-resolution remote sensing images (HRSI) is significant, yet
challenging. Recently, several research works have utilized the self-attention operation to …

Remote sensing image registration can benefit from a machine learning method based on
the likelihood of predicting semantic spatial position distributions. Semantic segmentation of …

Semantic segmentation of remote sensing imagery (RSI) has obtained great success with
the development of deep convolutional neural networks (DCNNs). However, most of the …

Featured Application In this paper, a Multi-Attribute Non-Maximum Suppression (MA-NMS)
algorithm, which adaptively adjusts suppression based on density and count attributes, is …