Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …

This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …

High-resolution remote sensing (RS) image change detection (CD) focuses on ground
surface changes. It has wide applications, including territorial spatial planning, urban region …

Due to its wide applications, remote sensing (RS) image semantic segmentation has
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …

The existing deep networks have shown excellent performance in remote sensing scene
classification (RSSC), which generally requires a large amount of class-balanced training …

Land use and land cover maps provide fundamental information that has been used in
different types of studies, ranging from public health to carbon cycling. However, the existing …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

Although deep learning has revolutionized remote sensing (RS) image scene classification,
current deep learning-based approaches highly depend on the massive supervision of …

Cloud cover is a common and inevitable phenomenon that often hinders the usability of
optical remote sensing (RS) image data and further interferes with continuous cartography …

In recent years, deep learning-based algorithms have brought great improvements to rigid
object detection. In addition to rigid objects, remote sensing images also contain many …