Building detection in SAR images based on fusion of classic and deep learning features.

Because of high-resolution imaging in day and night and any weather condition, Synthetic Aperture Radar (SAR) has various applications in remote sensing. Building detection is one of the important utilization of SAR images. With analysing SAR images information, a new framework based on fusion of statistical, texture, and semantic features is proposed. At first, in order to reduce the speckle noise effects on the classification results, the SAR image is segmented into superpixels which the classification process is performed for these segments. Then, the statistical Fisher and Haralick texture features are extracted and the best features are selected. By applying an adapted VGGNet, the third type of features are extracted. Using training samples, the features achieving the highest classification accuracy are selected and applied to the classifier. Then, the classification output is improved by decreasing the splitting of the large-size buildings and false alarms using morphological operations and contour fitting process. The performance of the proposed framework is evaluated with two different types of TerraSAR-X images of urban areas. Experiment results show that the proposed method has superior results than the similar works. Also, the proposed method has considerable small false alarm rate. [ABSTRACT FROM AUTHOR]