Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …

High spatial resolution (HSR) remote sensing (RS) images inevitably pose the challenge of
multiscale transformation, as small objects, such as cars and helicopters (HCs), may occupy …

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

Semantic segmentation of the remote sensing images (RSIs) has attracted increasing
interest in recent years. However, large-area segmentation of the woodland presents …

Classification tasks on land cover (LC) mapping are challenging due to the complex and
heterogeneous characteristics of remote sensing images (RSIs). Current LC classifications …

Currently, the most advanced high-resolution remote sensing image (HRRSI) semantic
labeling methods rely on deep neural networks. However, HRRSIs naturally have a serious …

Semisupervised learning is a forcible method to lessen the cost of annotation for remote
sensing semantic segmentation tasks. Recent related research works indicate that …

The rapid development of satellite platforms has yielded copious and diverse multisource
data for earth observation, greatly facilitating the growth of multimodal semantic …

Due to variable imaging conditions, resolution degradation often occurs in aerial images,
which in turn impairs the performance upper bound of semantic segmentation (SS). To solve …