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 …

In recent years, remote-sensing scene classification is one of the research hotspots and has
played an important role in the field of intelligent interpretation of remote-sensing data …

Scene classification of high spatial resolution (HSR) images can provide data support for
many practical applications, such as land planning and utilization, and it has been a crucial …

Scene classification is an indispensable part of remote sensing image interpretation, and
various convolutional neural network (CNN)-based methods have been explored to improve …

Scene classification is an active research topic in the remote sensing community, and
complex spatial layouts with various types of objects bring huge challenges to classification …

Convolutional neural networks (CNNs) have achieved great success when characterizing
remote sensing (RS) images. However, the lack of sufficient annotated data (together with …

Due to the proliferation of large-scale remote-sensing (RS) archives with multiple
annotations, multilabel RS scene classification and retrieval are becoming increasingly …

In remote sensing scene classification (RSSC), features can be extracted with different
spatial frequencies where high-frequency features usually represent detailed information …

Building footprint segmentation from high-resolution remote sensing (RS) images plays a
vital role in urban planning, disaster response, and population density estimation …

Significant progress has been achieved in remote sensing image scene classification
(RSISC) with the development of convolutional neural networks (CNNs) and vision …