Image and Signal Processing Methods

Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

[+] Author Affiliations
Richa Marwaha, Anil Kumar, Arumugam Senthil Kumar

Indian Institute of Remote Sensing, 4—Kalidas Road, Dehradun, Uttrakhand 248001, India

J. Appl. Remote Sens. 9(1), 095040 (Dec 17, 2015). doi:10.1117/1.JRS.9.095040
History: Received June 15, 2015; Accepted November 6, 2015
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Abstract.  Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Richa Marwaha ; Anil Kumar and Arumugam Senthil Kumar
"Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data", J. Appl. Remote Sens. 9(1), 095040 (Dec 17, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095040


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