With many platforms and sensors continuously observing the earth surface, the large
amount of remote sensing data presents a big data challenge. While remote sensing data …

Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …

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-resolution land cover mapping over large areas is a challenging task due to the lack of
high-quality labels. A potential solution is to leverage the existing knowledge contained in …

The success of deep learning in synthetic aperture radar (SAR) automatic target recognition
(ATR) relies on a large number of labeled samples; however, there are often wrong (noisy) …

There are thousands of observation satellites orbiting the Earth, each of which captures
massive-scale photographs covering millions of square kilometers everyday. In practice …

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

深度监督学习在合成孔径雷达自动目标识别任务中的成功依赖于大量标签样本. 但是,
在大规模数据集中经常存在错误(噪声) 标签, 很大程度降低网络训练效果 …

In this research, we present PerceptHashing, a technique designed to categorize million-
scale agricultural scenic images by incorporating human gaze shifting paths (GSPs) into a …

The ability to precisely identify the semantic categories of low-resolution (LR) aerial images
is a critical skill in the field of remote sensing. However, accurately performing this task is …