Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …

Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …

With the development of space-based imaging technology, a larger and larger number of
images with different modalities and resolutions are available. The optical images reflect the …

Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

This study presents a new end-to-end change detection network, called difference-
enhancement dense-attention convolutional neural network (DDCNN), that is designed for …

Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

This study aims to develop a deep learning based classification framework for remotely
sensed time series. The experiment was carried out in Yolo County, California, which has a …