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3 results for “Hyperspectral data processing”
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
Processing and Distribution of Hyperspectral Radiometer Data from Ships of Opportunity for Monitoring Phytoplankton in the Ocean
Ocean-colour sensors mounted on satellites can view the entire ocean over a period of a few days. However, to calibrate these sensors and validate the data, satellite observations must be compared with accurate and reliable in situ measurements, collected at the ocean surface. There are many regions of the ocean where these in situ measurements are rarely collected. Ocean-colour sensors can be mounted on research vessels and ships of opportunity. In this project, an ocean-colour sensor (Seabird HyperSAS Solar Tracker) was mounted on a yacht that visits remote regions of the planet where few observations have been collected. This project aims to process this data to a level for use by the scientific community for scientific applications, such as satellite validation.
SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS
SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning algorithm for the classification of hyperspectral data, Gaussian process expectation maximization (GP-EM), is proposed. Model parameters for each land cover class is first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture-of-Gaussians model. The mixture model is updated by expectationmaximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Two sets of hyperspectral data taken from the Botswana area by the NASA EO-1 satellite are used for experiments. Empirical evaluations show that the proposed framework performs significantly better than baseline algorithms that do not use spatial information, and the results are also better than any previously reported results by other algorithms on the same data.
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OpenNeuro
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