# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
###### Abstract
However, this method involved numerous geometric operations, potentially leading to topological issues in the generated seamline. [PERSON] et al. [12] introduced a Voronoi centerline-based seamline network generation method; however, this had high computational effort and occasional failures when dealing with complex graphs. Recent years have witnessed the emergence of diverse seamline generation methods, broadly categorized into pixel-based, object-based, and auxiliary data-based methods. Pixel-based methods focus on constructing a cost matrix for the image's overlapping area and selecting search strategies for optimal mosaic generation. Cost matrices commonly consider gray scale, texture, gradient differences, and other factors. Commonly used search strategies include dynamic programming algorithms [13], [PERSON]'s algorithm [14], bottleneck models [15], snake models [16], ant colony algorithms [17], and graph cut models [18]. For example, [PERSON] [19] proposed an energy-minimizing method using the twin snakes model, yet faced challenges in non-convergence, particularly in forested areas. [PERSON] et al. [20] employed [PERSON]'s algorithm and the minimax algorithm, but they required specified start and end points. [PERSON] and [PERSON] [21] used various graph-cutting methods for seamline generation. Meanwhile, [PERSON] et al. [22] implemented a continuous space ant colony algorithm based on gray difference and gradient, albeit with limitations in optimizing the vertices of unordered paths. Object-based methods integrate object-level information, such as image segmentation, feature classification, and change detection, to refine seamline generation. [PERSON] [23] utilized a watershed algorithm to segment images and identify seamlines along feature boundaries, mitigating potential mosaic seam issues. Other methods involve segmentation-based urban orthophoto mosaic generation [24], watershed segmentation-based optimization [25], and determining orthophoto seamlines using regional rate of change [26]. Additionally, superpixel segmentation has been applied, utilizing a graph cut algorithm to obtain optimal seamlines [27]. Auxiliary data-based methods leverage external information, such as point cloud data, digital surface models (DSM), and vector data, to guide seamlinedirection. Examples include using laser radar point cloud for seamline detection [28], road vector data for seamline determination [29], and elevation information from DSM for seamline direction [30]. [PERSON] et al. [31] employed vector road maps to construct a weighted graph, using [PERSON]'s algorithm to find optimal paths as seamless. In a similar vein, [PERSON] et al. [32] proposed determining seamlines based on road probability maps, obtained using a D-LinkNet neural network and post-processing. The preferred road areas are identified through binarizing the road probability maps in overlapping areas, and [PERSON]'s algorithm determines the optimal seamline. Presently, the majority of seamline generation methods focuses on optical images, overlooking the distinct characteristic of SAR images, which are susceptible to random speckle noise inherent in SAR data, potentially impacting processing outcomes. Recognizing the limitations of pixel-based information in fully distinguishing feature targets and obtaining high-quality auxiliary data, an object-based method emerges as a suitable method for seamline generation. In response, a segmentation-based optimal seamline generation (SOSG) method for SAR image mosaics is proposed, aiming to create seamless SAR mosaic images. For multiple SAR images, the proposed SOSG method follows a systematic method. Initially, SAR image registration based on the rational function model and radiometric normalization processing is completed. Subsequently, joint segmentation of overlapping areas between images is implemented. Building on these joint segmentation results, the screening of homogeneous areas is performed.