Fast and Robust Registration of Multimodal Remote Sensing Images via Dense Orientated Gradient Feature
Abstract. This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g. optical, LiDAR, SAR and map) The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel wise feature descriptor named Dense Orientated Gradient Histogram (DOGH) which can be computed effectively at every pixel and is robust to non linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT) technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state of the art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000 x 20000 pixels) Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS) in both registration accuracy and computational efficiency.