Search

The scale parameter (SP), to control the sizes of objects, is of great significance in multiscale segmentation which is a prerequisite and foundational step for object-based change detection (OBCD). However, the appropriate SP is not readily apparent and the majority of the existing OBCD algorithms obtain the SPs by empirical or subjective trial-and-error ways that may lead to dissatisfactory accuracy and be time-consuming. To address this issue, an automatic approach for optimal segmentation scale selection for OBCD is proposed in this letter. First, a changed fuzziness image for bitemporal images was generated. Second, multiscale segmentation was implemented in a series of candidate scales, and the merging relationships between adjacent scales were built. Then, mapping the segments to the previous fuzziness image, a statistical metric describing the homogeneity of objects based on the Kullback–Leibler divergence was defined, the increments of the metric between merged objects and their child objects were calculated and weighted to identify the optimal SPs. For performance evaluation, Dempter–Shafer (DS) evidence fusion was utilized in the scales selected by the proposed approach in comparison with other state of art or empirical ones. The experimental results employing GF-1, Google Earth, and aerial images demonstrated the superiority and effectiveness of the SPs identified by the proposed approach in the OBCD task. [ABSTRACT FROM AUTHOR]

Multimodal image registration is the fundamental technique for scene analysis with series remote sensing images of different spectrum region. Due to the highly nonlinear radiometric relationship, it is quite challenging to find common features between images of different modal types. This paper resorts to the deep neural network, and tries to learn descriptors for multimodal image patch matching, which is the key issue of image registration. A Siamese fully convolutional network is set up and trained with a novel loss function, which adopts the strategy of maximizing the feature distance between positive and hard negative samples. The two branches of the Siamese network are connected by the convolutional operation, resulting in the similarity score between the two input image patches. The similarity score value is used, not only for correspondence point location, but also for outlier identification. A generalized workflow for deep feature based multimodal RS image registration is constructed, including the training data curation, candidate feature point generation, and outlier removal. The proposed network is tested on a variety of optical, near infrared, thermal infrared, SAR, and map images. Experiment results verify the superiority over other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Saliency, the distinctive parts of an image, has shown good potential for many applications (e.g., image interpretation and target detection). In this letter, a novel saliency detection method based on background context-aware is proposed for synthetic aperture radar (SAR) images. According to a statistical analysis of SAR image characteristics, several reference background patches (RBPs) are selected. Then, the dissimilarities between the current patch and the RBPs are used to calculate the local saliency, which is further enhanced for the final saliency under the multiscale framework. Experimental results demonstrate the effectiveness of the proposed method, which outperforms some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This letter presents an effective approach for synthetic aperture radar (SAR) image registration. Corresponding edge features are extracted by exploring the local spatial relationship around corresponding speeded-up robust feature points. Only edge pairs that are matched in the aspect of distance and orientation to the center feature points are taken as effective edge features. For edge-point set matching, we introduce the coherent point drift (CPD) registration method. CPD is able to maintain the global topological structure of edges and is robust to outliers. We refine the original CPD by taking our confidence on edge points as prior information. A new objective function is deduced, resulting in a better registration performance. Experimental results show that the proposed method performs well in terms of registration robustness and accuracy. [ABSTRACT FROM PUBLISHER]