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 …
Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges
5G wireless communication networks are currently being deployed, and B5G networks are
expected to be developed over the next decade. AI technologies and, in particular, ML have …
expected to be developed over the next decade. AI technologies and, in particular, ML have …
Deep forest
Current deep-learning models are mostly built upon neural networks, ie multiple layers of
parameterized differentiable non-linear modules that can be trained by backpropagation. In …
parameterized differentiable non-linear modules that can be trained by backpropagation. In …
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 …
BigEarthNet-MM: A large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]
G Sumbul, A De Wall, T Kreuziger… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
This article presents the multimodal BigEarthNet (BigEarthNet-MM) benchmark archive
consisting of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support deep …
consisting of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support deep …
Image retrieval from remote sensing big data: A survey
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 …
increasing remote sensing imaging sensors on these platforms, has led to the formation of …
Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
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 …
optical remote sensing (RS) image data and further interferes with continuous cartography …
Recent developments of content-based image retrieval (CBIR)
X Li, J Yang, J Ma - Neurocomputing, 2021 - Elsevier
With the development of Internet technology and the popularity of digital devices, Content-
Based Image Retrieval (CBIR) has been quickly developed and applied in various fields …
Based Image Retrieval (CBIR) has been quickly developed and applied in various fields …
Looking closer at the scene: Multiscale representation learning for remote sensing image scene classification
Remote sensing image scene classification has attracted great attention because of its wide
applications. Although convolutional neural network (CNN)-based methods for scene …
applications. Although convolutional neural network (CNN)-based methods for scene …
Multilabel remote sensing image retrieval based on fully convolutional network
Conventional remote sensing image retrieval (RSIR) system usually performs single-label
retrieval where each image is annotated by a single label representing the most significant …
retrieval where each image is annotated by a single label representing the most significant …