Shen Xiaole, Guo Yiquan, Cao Jinzhou
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
PeerJ Comput Sci. 2023 Mar 15;9:e1290. doi: 10.7717/peerj-cs.1290. eCollection 2023.
Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction.
多尺度分割（MSS）在基于对象的图像分析方法（OBIA）中至关重要。如何描述遥感图像的潜在特征并结合多种特征进行基于对象的多尺度图像分割是OBIA领域的一个热点。传统的基于对象的分割方法大多使用遥感图像的光谱和形状特征，而较少关注纹理和边缘特征。我们分析了传统图像分割方法和基于对象的MSS方法。然后，在比较图像纹理特征描述方法的基础上，提出了一种基于时频分析的遥感图像纹理特征描述方法。此外，在此基础上构建了一种测量图像对象纹理异质性的方法。以自下而上的区域合并作为MSS策略，提出了一种结合纹理特征的遥感图像基于对象的MSS算法。最后，基于遥感图像的边缘特征，提出了一种遥感图像边缘强度描述方法和边缘融合代价准则。结合异质性准则，提出了一种结合光谱、形状、纹理和边缘特征的基于对象的MSS算法。实验结果表明，本文提出的基于综合特征的对象MSS算法在分割具有丰富纹理信息和细长形状的地面物体时能够获得更完整的分割对象，且不易出现过分割现象。与传统的基于对象的分割算法相比，该算法的平均准确率提高了4.54%，区域比接近1，这将更有利于后续遥感图像的处理和分析。此外，本文提出的基于对象的MSS算法能够有效地获取更完整的地面物体，可广泛应用于建筑物提取等场景。
