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IEEE Geoscience and Remote Sensing Letters, 2014
In this letter, a new endmember extraction algorithm based on adaptive differential evolution (DE) (ADEE) is proposed for hyperspectral remote sensing imagery. In the proposed algorithm, the endmember extraction is transformed into a combinatorial optimization problem through constructing the objective function by minimizing the root mean square error ...
Liangpei Zhang, Yanfei Zhong
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In this letter, a new endmember extraction algorithm based on adaptive differential evolution (DE) (ADEE) is proposed for hyperspectral remote sensing imagery. In the proposed algorithm, the endmember extraction is transformed into a combinatorial optimization problem through constructing the objective function by minimizing the root mean square error ...
Liangpei Zhang, Yanfei Zhong
exaly +2 more sources
Underwater target detection with hyperspectral remote-sensing imagery
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010This paper presents a new way of detecting underwater targets with hyperspectral remote-sensing data. The idea is to use a bathymetric model of subsurface reflectance to correct the spectral distortions due to water crossing. Then we derive the Matched filter (MF) from the Likelihood Ratio Test (LRT) built to decide whether the target is present or ...
Sylvain Jay, Mireille Guillaume
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Superpixel-Guided Sparse Unmixing for Remotely Sensed Hyperspectral Imagery
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Sparse representation-based approaches have been successfully applied to remotely sensed hyperspectral image unmixing. In recent years, sparse unmixing techniques have incorporated spatial information into the sparse unmixing model, achieving improved fractional abundance results.
Shaoquan Zhang +6 more
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Feature-Driven Multilayer Visualization for Remotely Sensed Hyperspectral Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2010Displaying 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. In this paper, we present a feature-driven multilayer visualization technique that automatically chooses data visualization techniques ...
Shangshu Cai +2 more
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A New Digital Repository for Remotely Sensed Hyperspectral Imagery on GPUs
2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2013Hyperspectral imaging is a new technique in remote sensing in which an imaging spectrometer collects hundred of images (at different wavelength channels) for the same area on the surface of Earth. Over the last years, hyperspectral image data sets have been collected from a great amount of locations over the world using a variety of instruments for ...
Jorge Sevilla Cedillo, Antonio Plaza
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An Efficient Classifier Design for Remote Sensing Hyperspectral Imagery
2015 7th International Conference on Recent Advances in Space Technologies (RAST), 2015Among the various classifiers, the Support Vector Data Description (SVDD) is a well-known strong classifier since it uses nonparametric boundary approach that constructs the minimum hypersphere enclosing the target objects as much as possible. The SVDD has been used in many studies for classification, anomaly and target detection problems on airborne ...
BAL, Abdullah, Binol, Hamidullah
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Support Vector Machine for Classification of Hyperspectral Remote Sensing Imagery
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007As one of the popular and advanced statistical learning algorithms, support vector machine (SVM) has been the new hot study area of pattern recognition and machine learning in recent years. SVM has such advantages as suitableness to high dimensional data, requirement of few samples and robustness to uncertainty, so it can be used to hyperspectral ...
Chen-guang Dai +2 more
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A multi-manifold clustering algorithm for hyperspectral remote sensing imagery
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Unsupervised classification plays a key role in remote sensing hyperspectral image analysis. Complexities arise from the high dimensionality of hyperspectral imagery and this implies the need for dimensionality reduction as a vital preprocessing step.
Aidin Hassanzadeh +2 more
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Chlorophyll content retrieval from hyperspectral remote sensing imagery
Environmental Monitoring and Assessment, 2015Chlorophyll content is the essential parameter in the photosynthetic process determining leaf spectral variation in visible bands. Therefore, the accurate estimation of the forest canopy chlorophyll content is a significant foundation in assessing forest growth and stress affected by diseases.
Xiguang, Yang, Ying, Yu, Wenyi, Fan
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Dehazing method for hyperspectral remote sensing imagery with hyperspectral linear unmixing
SPIE Proceedings, 2016Haze always exists in hyper-spectral remote sensing imagery, and it is a key reason that influences the effective information extraction of hyper-spectral images. Specially, when the faint haze covers part of the target in remote sensing images, the target still can be detected but not clear.
Yuquan Gan +3 more
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