Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary
ability of convolutional neural networks (CNN) in creating semantic, high-level and …

Forest fires pose a significant threat to both the economy and ecology, causing extensive
damage. Smoke serves as a crucial indicator of forest fires, often appearing before the …

Semantic segmentation is a crucial image understanding task, where each pixel of image is
categorized into a corresponding label. Since the pixel-wise labeling for ground-truth is …

Colonoscopy is the gold standard method for investigating the gastrointestinal tract.
Localizing the polyps in colonoscopy images plays a vital role when doing a colonoscopy …

Abstract Background and Objective: Image-guided clinical diagnosis can be achieved by
automatically and accurately segmenting prostate and prostatic cancer in male pelvic …

It is very challenging to accurately segment smoke images because smoke has some
adverse properties, such as semi-transparency and blurry boundary. Aiming at solving these …

Land cover classification is a multiclass segmentation task to classify each pixel into a
certain natural or human-made category of the earth's surface, such as water, soil, natural …

An appropriate detection network is required to extract building information in remote
sensing images and to relieve the issue of poor detection effects resulting from the …

The presence of clouds in satellite imagery may pose hindrances to the accurate and
reliable analysis of the objects present on the land. Therefore, automatic cloud detection is a …

Built-up areas and buildings are two main targets in remote sensing research; consequently,
automatic extraction of built-up areas and buildings has attracted extensive attention. This …