The past decade has witnessed many great successes of machine learning (ML) and deep
learning (DL) applications in agricultural systems, including weed control, plant disease …

Recently, self-supervised large-scale visual pre-training models have shown great promise
in representing pixel-level semantic relationships, significantly promoting the development …

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting
changes in the Earth's surface, finding wide applications in urban planning, disaster …

Weakly-supervised image segmentation has recently attracted increasing research
attentions, aiming to avoid the expensive pixel-wise labeling. In this paper, we present an …

Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of
urban development. The post-classification comparison (PCC) is a widely-used change …

Recent years have witnessed the superiority of deep learning-based algorithms in the field
of HSI classification. However, a prerequisite for the favorable performance of these …

Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and
Segment Anything (SAM) have demonstrated impressive zero-shot performance on image …

Weakly-supervised segmentation with label-efficient sparse annotations has attracted
increasing research attention to reduce the cost of laborious pixel-wise labeling process …

The rapid development of deep learning has driven significant progress in the field of image
semantic segmentation-a fundamental task in computer vision. Semantic segmentation …

Change detection (CD) using deep learning techniques is a trending topic in the field of
remote sensing; however, most existing networks require pixel-level labels for supervised …