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 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.