There has been a recent emphasis on integrating physical models and deep neural
networks (DNNs) for SAR target recognition, to improve performance and achieve a higher …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Road extraction from a remote sensing image is a research hotspot due to its broad range of
applications. Despite recent advancements, achieving precise road extraction remains …

Synthetic aperture radar (SAR) target recognition is a fundamental task in SAR image
interpretation, which has made tremendous progress with the advancement of artificial …

Deep learning is widely applied in synthetic aperture radar (SAR) target recognition.
However, the high cost of collecting real SAR target data leads to insufficient data volume …

Deep neural networks (DNNs) have obtained remarkable achievements in various vision
tasks. However, DNNs' mechanism remains obscure, especially in synthetic aperture radar …

General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods
perform well under standard operation conditions (SOCs). However, they are not effective in …

Training deep learning models on remote sensing imagery is an increasingly popular
approach for addressing pressing challenges related to urbanization extreme weather …

卷积神经网络(CNN) 在合成孔径雷达(SAR) 图像目标分类任务中应用广泛. 由于网络工作机理
不透明, CNN 模型难以满足高可靠性实际应用的要求. 类激活映射方法常用于可视化CNN …

The characteristics of synthetic aperture radar (SAR) images are easily affected by factors
such as sensor parameters, imaging scenes, etc., which may lead to data distributional …