The extraction of urban structures such as buildings from very high-resolution (VHR) remote
sensing imagery has improved dramatically, thanks to recent developments in deep …

Satellite image time series classification has become a research focus with the launch of
new remote sensing sensors capable of capturing images with high spatial, spectral, and …

With the rapid development of Light Detection And Ranging (LiDAR) systems, the novel dual-
channel airborne LiDAR systems have emerged to provide more complete and precise data …

This paper presents exploratory work looking into the effectiveness of attention mechanisms
(AMs) in improving the task of building segmentation based on convolutional neural network …

Most of the existing approaches to the extraction of buildings from high-resolution
orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise …

Despite the good results that have been achieved in unimodal segmentation, the inherent
limitations of individual data increase the difficulty of achieving breakthroughs in …

Speech enhancement aims to improve the listening quality and intelligibility of noisy speech
in adverse environments. It proves to be challenging to perform speech enhancement in …

The semantic segmentation of high-resolution remote sensing imagery (RSI) is an essential
task for many applications. As a promising unsupervised learning method, unsupervised …

Large-scale crop mapping from dense time-series images is a difficult task and becomes
even more challenging with the cloud coverage. Current deep learning models frequently …

Despite the recent advances of deep neural networks, object detection for adverse weather
remains challenging due to the poor perception of some sensors in adverse weather …