Abstract
We propose an automatic region-based registration method for remote sensing imagery. In this method, we aim to register two images by matching region properties to address possible errors caused by local feature estimators. We apply automated image segmentation to identify the regions and calculate regional Fourier descriptors and standardized regional intensity descriptors for each region. We define a joint matching cost, as a linear combination of Euclidean distances, to establish and extract correspondences between regions. The segmentation technique utilizes kernel density estimators for edge localization, followed by morphological reconstruction and the watershed transform. We evaluated the registration performance of our method on synthetic and real datasets. We measured the registration accuracy by calculating the root-mean-squared error (RMSE) between the estimated transformation and the ground truth transformation. The results obtained using the joint intensity-Fourier descriptor were compared to the results obtained using Harris, Minimum eigenvalue, Features accelerated segment test (FAST), speeded-up robust features (SURF), binary robust invariant scalable keypoints (BRISK) and KAZE keypoint descriptors. The joint intensity-Fourier descriptor yielded average RMSE of 0.446 ± 0.359 pixels and 1.152 ± 0.488 pixels on two satellite imagery datasets consisting of 35 image pairs in total. These results indicate the capacity of the proposed technique for high accuracy. Our method also produces a lower registration error than the compared feature-based methods.