17 December 2015 Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data
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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.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Richa Marwaha, Anil Kumar, Arumugam Senthil Kumar, "Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data," Journal of Applied Remote Sensing 9(1), 095040 (17 December 2015). https://doi.org/10.1117/1.JRS.9.095040 . Submission:
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