Due to its various application potentials, the remote sensing image scene classification
(RSSC) has attracted a broad range of interests. While the deep convolutional neural …

In recent years, supervised learning has been widely used in various tasks of optical remote
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …

As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label
remote sensing image scene classification (MLRSSC) is attracting increasing research …

The development of accurate methods for multi-label scene classification (MLC) of remote
sensing (RS) images is one of the most important research topics in RS. To address MLC …

High spatial resolution remote sensing (HSRRS) images contain complex geometrical
structures and spatial patterns, and thus HSRRS scene classification has become a …

The scene classification of a remote sensing image has been widely used in various fields
as an important task of understanding the content of a remote sensing image. Specially, a …

This review article details the advancements in detecting heavy metals in aquatic
environments using remote sensing methodologies. Heavy metals are significant pollutants …

The performance of deep neural networks depends on the accuracy of labeled samples, as
they usually contain label noise. This study examines the semantic segmentation of remote …

This paper presents a systematic approach for solving complex prediction problems with a
focus on energy efficiency. The approach involves using neural networks, specifically …

Distilling actionable patterns from large-scale streaming data in the presence of concept drift
is a challenging problem, especially when data is polluted with noisy labels. To date, various …