Results 181 to 190 of about 2,900 (227)

Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2016
In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which ...
Xian Guo, Xin Huang, Lefei Zhang, Liangpei Zhang, Antonio Plaza, Jon Atli Benediktsson   +5 more
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Hyperspectral Remote Sensing Imagery Processing Focused on Forest Applications

International Review of Aerospace Engineering (IREASE), 2017
Imaging spectrometers with hundreds of spectral channels in visible and infrared regions are designed by various companies to enhance the information content of the relevant hyperspectral imagery processing compared to common-used multispectral systems.
Vladimir V. Kozoderov, Timofei V. Kondranin, Egor V. Dmitriev   +2 more
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Object-oriented subspace analysis for airborne hyperspectral remote sensing imagery

Neurocomputing, 2010
An 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, Xin Huang
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Remote sensing with hyperspectral imagery using DASI-an imaging interferometer

IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium, 2002
Describes an approach to terrestrial remote sensing using a novel technique, imaging interferometry. A practical implementation of the instrument, the Digital Array Scanned Interferometer (DASI), has been under development at the authors' laboratories. An overview of recent terrestrial scenes measured using an airborne DASI sensor is presented.
P.D. Hammer, D.L. Peterson, W.H. Smith, S.E. Dunagan, L.F. Johnson, A.W. Strawa, J. Zott, G. Langford   +7 more
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Junk Bands Recovery for Hyperspectral Remote Sensing Imagery via Contourlet

2008 ISECS International Colloquium on Computing, Communication, Control, and Management, 2008
The levels of noise in Hyperspectral data are quite different from band to band. Junk band refers to the band which is so noisy that it is usually discarded in data analysis. Considering that the profiles of bands at close wavelengths are quite similar and the conlourlet is good at capturing profiles, we propose a junk band recovery algorithm for ...
Lei Sun, De-feng Gu, Jian-shu Luo
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A multi-manifold clustering algorithm for hyperspectral remote sensing imagery

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Unsupervised 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, Tuomo Kauranne, Arto Kaarna   +2 more
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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), 2020
Effective 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, Carlos Torres, Oytun Ulutan, Alana Ayasse, Dar Roberts, B.S. Manjunath   +5 more
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Independent-component analysis for hyperspectral remote sensing imagery classification

Optical Engineering, 2006
We investigate the application of independent-component analysis ICA to remotely sensed hyperspectral image classification. We focus on the performance of two well-known and frequently used ICA algorithms: joint approximate diagonalization of eigenmatrices JADE and FastICA; but the proposed method is applicable to other ICA algo- rithms.
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Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Remote Sensing of Environment, 2008
Abstract Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands
Yongqin Zhang, Jing M. Chen, John R. Miller, Thomas L. Noland   +3 more
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An Understanding Rice Hyperspectral Remote Sensing Imagery Classification Framework

2019
The staple food for the Indonesian people is rice because in rice it contains a large number of calories for the intake of more than 200 million people. Hyperspectral is the sensors that can be used in a variety of applications, one of which is for rice monitoring.
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