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Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020Effective analysis of hyperspectral imagery is essential for gathering fast and actionable information of large areas affected by atmospheric and green house gases. Existing methods, which process hyperspectral data to detect amorphous gases such as CH 4 require manual inspection from domain experts and annotation of massive datasets. These methods do
Satish Kumar +5 more
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M2H-Net: A Reconstruction Method For Hyperspectral Remotely Sensed Imagery
ISPRS Journal of Photogrammetry and Remote Sensing, 2021Abstract Hyperspectral remote sensing can get spatially and spectrally continuous data simultaneously. However, the imaging equipment is usually expensive and complex, along with the low spatial resolution. In recent years, reconstruction of hyperspectral image by deep learning from the widely used low-cost, high spatial resolution RGB camera, has ...
Lei Deng +6 more
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An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2006A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing.
Yanfei Zhong +3 more
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A Neural Approach to Compression of Hyperspectral Remote Sensing Imagery
2001This paper presents an original research for hyperspectral satellite image compression using a fully neural system with the following processing stages: (1) a Hebbian network performing the principal component selection; (2) a system of "k" circular self-organizing maps for vector quantization of the previously extracted components.
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A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery
IEEE Geoscience and Remote Sensing Letters, 2017Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have ...
Han Zhai +4 more
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Object-oriented subspace analysis for airborne hyperspectral remote sensing imagery
Neurocomputing, 2010An object-oriented mapping approach based on subspace analysis of airborne hyperspectral images was investigated in this paper. Hyperspectral features were extracted based on subspace learning approaches, in order to reduce the redundancy of spectral space and extract the characteristic images for the further object-oriented classification.
Liangpei Zhang 0001, Xin Huang 0002
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An Evaluation of Visualization Techniques for Remotely Sensed Hyperspectral Imagery
2011Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. Currently no approach can satisfactorily render the desired information at arbitrary levels of detail. This chapter discusses user studies on several approaches for representing the information contained in hyperspectral information.
Shangshu Cai, Robert Moorhead, Qian Du
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Pattern Recognition Letters, 2016
Abstract This paper reviews the state-of-the-art representation-based classification and detection approaches for hyperspectral remote sensing imagery, including sparse representation-based classification (SRC), collaborative representation-based classification (CRC), and their extensions.
Wei Li 0032, Qian Du 0001
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Abstract This paper reviews the state-of-the-art representation-based classification and detection approaches for hyperspectral remote sensing imagery, including sparse representation-based classification (SRC), collaborative representation-based classification (CRC), and their extensions.
Wei Li 0032, Qian Du 0001
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Estimating the Intrinsic Dimensionality of Hyperspectral Remote Sensing Imagery with Rare Features
2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), 2018Estimating the intrinsic dimensionality of hyper spectral remote sensing imagery is an essential step in processing this kind of data. A novel estimation algorithm is proposed, which can preserve both abundant and rare features in original data.
Xin Luo +3 more
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Dive into Aerial Remote Sensing Underwater Depth Estimation with Hyperspectral Imagery
Proceedings of the AAAI Conference on Artificial IntelligenceVisible spectrum images capture limited information from just three discrete bands, often resulting in suboptimal performance in underwater depth estimation (UDE) due to significant information loss from water absorption. In contrast, HSIs, which include hundreds of continuous bands, provide abundant spectral information that offers greater resilience
Jiahao Qi +5 more
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