1.7k citations · 10.4k across the 63 of their papers we have counts for
13 papers · 1 filter
UIU-Net: U-Net in U-Net for Infrared Small Object Detection
Xin Wu, Danfeng Hong, Jocelyn Chanussot
Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distingui…
Gas kinematics around filamentary structures in the Orion B cloud
Mathilde Gaudel, Jan H. Orkisz, Maryvonne Gerin +27
Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young hi…
Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection
Jules BOURCIER, Thomas Floquet, Gohar Dashyan +3
In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational…
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery
Jules Bourcier, Gohar Dashyan, Jocelyn Chanussot +1
The application of deep neural networks to remote sensing imagery is often constrained by the lack of ground-truth annotations. Adressing this issue requires models that generalize…
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti +2
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural fo…
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Nassim Ait Ali Braham, Lichao Mou, Jocelyn Chanussot +2
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize…
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation
Na Liu, Wei Li, Yinjian Wang +3
The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. Howe…
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Minghua Wang, Danfeng Hong, Zhu Han +5
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the ob…
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +2
Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detec…
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
Danfeng Hong, Jing Yao, Deyu Meng +2
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perf…
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review
Jiaxin Li, Danfeng Hong, Lianru Gao +4
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily…
Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
Jun Yue, Leyuan Fang, Pedram Ghamisi +4
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise…
Multimodal Fusion Transformer for Remote Sensing Image Classification
Swalpa Kumar Roy, Ankur Deria, Danfeng Hong +3
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs). As a result, m…