Urban Shadow Detection and Classification Using Hyperspectral Image

Shadow is an inevitable problem in high-resolution remote sensing images. There are need and significance in extracting information from shadow-covered areas, such as in land-cover mapping. Although the illumination energy of shadow pixels is low, hyperspectral image can provides rich enough band in...

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Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2017-12, Vol.45 (6), p.945-952
Main Authors: Qiao, Xiaojun, Yuan, Deshuai, Li, Hui
Format: Article
Language:English
Subjects:
Classification
Classifiers
Earth and Environmental Science
Earth Sciences
Ground truth
Illumination
Image classification
Image detection
Image resolution
Land cover
Land use
Mapping
Pixels
Remote sensing
Remote Sensing/Photogrammetry
Research Article
Spectra
Support vector machines
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Summary:Shadow is an inevitable problem in high-resolution remote sensing images. There are need and significance in extracting information from shadow-covered areas, such as in land-cover mapping. Although the illumination energy of shadow pixels is low, hyperspectral image can provides rich enough band information to differentiate various urban targets/materials and to classify them. This study firstly analyzes the spectra difference between shadow and non-shadow classes so as to detect shadow-pixel. To classify the shadow pixels, Spectral Angle Mapper (SAM) method was adopted to classify urban land-cover mapping, because it can reduce the influence resulted from different illumination intensity. Then, training samples were collected among different classes from the shadow pixels, and their Jeffries–Matusita (J–M) distance were computed to validate the spectral separability among classes, with the square distances of J–M among classes all bigger than 1.9. Finally, Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) classifier were used to classify all the shadow pixels as different land-cover types. The results showed MLC and SVM outperform the SAM in classifying similar classes. The classification result in SVM was validated to find having conformity with ground truth.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-016-0649-3