Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Transformers in remote sensing: A survey
Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …
sensing image analysis over the past decade. Recently, transformer-based architectures …
Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities
Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …
with a set of semantic categories based on their contents, has broad applications in a range …
Remote sensing for agricultural applications: A meta-review
Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …
for human livelihood. Today, this role must be satisfied within a context of environmental …
Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …
[HTML][HTML] A review of supervised object-based land-cover image classification
L Ma, M Li, X Ma, L Cheng, P Du, Y Liu - ISPRS Journal of Photogrammetry …, 2017 - Elsevier
Object-based image classification for land-cover mapping purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …
imagery has attracted significant attention in recent years. Numerous studies conducted over …
AID: A benchmark data set for performance evaluation of aerial scene classification
Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …
semantic category, is a fundamental problem for understanding high-resolution remote …
A survey of machine learning for big data processing
There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …
domains. While the potential of these massive data is undoubtedly significant, fully making …
[HTML][HTML] Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review
Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …
Towards better exploiting convolutional neural networks for remote sensing scene classification
We present an analysis of three possible strategies for exploiting the power of existing
convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they …
convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they …