NDLI: An evaluation of class knowledge transfer from synthetic to real hyperspectral imagery

Content Provider	IEEE Xplore Digital Library
Author	Bue, B.D. Merenyi, E. Csatho, B.
Copyright Year	2011
Description	Author affiliation: Department of Statistics, Rice University, Houston, TX, 77006 (Merenyi, E.) \|\| Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77006 (Bue, B.D.) \|\| Department of Geology, University at Buffalo, SUNY, Buffalo, NY (Csatho, B.)
Abstract	Hyperspectral imagery provides ample signal content to identify and distinguish between spectrally similar, but compositionally unique, materials, but representative training samples for all materials in a given scene are often unavailable in remote sensing applications. We propose a technique which leverages training spectra of materials from one hyperspectral image (the source), for classifying another (target) hyperspectral image, allowing robust target detection of source classes in the target image. By locating spectra of known material species in the source image which are also known in the target image, we derive a transformation that compensates for systematic spectral differences between the images, including atmospheric or seasonal effects, calibration differences, and noise. With this transformation — applied as a similarity measure — we can adapt a classifier trained on spectra from the source image, to classify the target image. We evaluate our technique between a pair of synthetic hyperspectral images with systematic differences in the spectral signatures of corresponding materials. Then we assess the feasibility of using synthetic imagery for training a classifier to identify spectral species in similar, real images, and show knowledge transfer results between a synthetic (HYDICE-type) source image and a real low-altitude AVIRIS target image of a complex urban area.
Starting Page	1
Ending Page	4
File Size	633223
Page Count	4
File Format	PDF
ISBN	9781457722028
ISSN	21586268
e-ISBN	9781457722011
DOI	10.1109/WHISPERS.2011.6080872
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-06
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training DIRSIG AVIRIS Accuracy Synthetic Transfer learning Materials Object detection Domain adaptation HYDICE Knowledge transfer Hyperspectral imaging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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