Semantic segmentation for unmanned aerial vehicle (UAV) remote sensing images has
become one of the research focuses in the field of remote sensing at present, which could …

In remote sensing images (RSIs), accurate semantic segmentation faces more challenges
because of small targets, unbalanced categories, and complex scenes. Restricted by local …

Semantic segmentation is a significant task for remote sensing interpretation, which takes
advantage of contextual semantic information to classify each pixel into a specific category …

Capturing accurate multiscale semantic information from the images is of great importance
for high-quality semantic segmentation. Over the past years, a large number of methods …

Although deep learning-based methods for semantic segmentation have achieved
prominent performance in the general image domain, semantic segmentation for high …

Semantic segmentation of remote sensing images encounters four significant difficulties: 1)
complex backgrounds, 2) large-scale differences, 3) numerous small objects, and 4) …

Deeplabv3+ currently is the most representative semantic segmentation model. However,
Deeplabv3+ tends to ignore targets of small size and usually fails to identify precise …

Semantic segmentation plays a vital role in the segmentation of remote sensing field for its
wide range of applications. The major current method for segmentation of remotely sensed …

Since DCNNs (deep convolutional neural networks) have been successfully applied to
various academic and industrial fields, semantic segmentation methods, based on DCNNs …

Semantic segmentation labels each pixel in high-resolution remote sensing (HRRS) images
with a category. To tackle with the large size and complexity of HRRS images, this letter …