Urban Shadow Detection and Classification Using Hyperspectral Image
| العنوان: | Urban Shadow Detection and Classification Using Hyperspectral Image |
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| المؤلفون: | Hui Li, Deshuai Yuan, Xiaojun Qiao |
| المصدر: | Journal of the Indian Society of Remote Sensing. 45:945-952 |
| بيانات النشر: | Springer Science and Business Media LLC, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Ground truth, 010504 meteorology & atmospheric sciences, Pixel, business.industry, Geography, Planning and Development, 0211 other engineering and technologies, Maximum likelihood classifier, Hyperspectral imaging, 02 engineering and technology, 01 natural sciences, Support vector machine, Geography, Classification result, Earth and Planetary Sciences (miscellaneous), Computer vision, Artificial intelligence, business, Classifier (UML), Spectral angle, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences |
| الوصف: | 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. |
| تدمد: | 0974-3006 0255-660X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::0cf72c24a94b099c2a2e89d534bcf634 https://doi.org/10.1007/s12524-016-0649-3 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........0cf72c24a94b099c2a2e89d534bcf634 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 09743006 0255660X |
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