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 …
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 …
LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation
Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
Classification of remote sensing images using EfficientNet-B3 CNN model with attention
Scene classification is a highly useful task in Remote Sensing (RS) applications. Many
efforts have been made to improve the accuracy of RS scene classification. Scene …
efforts have been made to improve the accuracy of RS scene classification. Scene …
Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Land-cover classification with high-resolution remote sensing images using transferable deep models
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are
available for land-cover mapping. However, due to the complex information brought by the …
available for land-cover mapping. However, due to the complex information brought by the …
When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs
Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …
many applications. More recently, with the advances of deep learning models especially …
Perceiving spectral variation: Unsupervised spectrum motion feature learning for hyperspectral image classification
Y Sun, B Liu, X Yu, A Yu, K Gao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …
achieved significant development. The superior capability of feature extraction from these …
Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation
Due to its wide applications, remote sensing (RS) image semantic segmentation has
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …
[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 …