高分辨率遥感影像特征分割及算法评价分析

图像分割一直是图像处理和计算机视觉领域中的一项关键技术。本文首先从遥感影像地学处理与应用的角度阐述了影像分割技术对于遥感信息提取和目标识别的重要性,然后提出了基于特征的高分辨率遥感影像信息提取技术框架,建立了一套基于特征的遥感影像分割方法及分类体系。同时,鉴于遥感影像分割方法评价的重要性, 阐述了一种高分辨率遥感影像分割方法评价的思路,并对几种典型的基于特征的遥感影像分割方法进行定性和定量的试验和评价,对其各自的性能和适用面进行对比分析。最后,指出了遥感影像特征分割方法所存在的问题及其发展趋势。

Research on High Resolution Remote Sensing Image Segmentation Methods Based on Features and Evaluation of Algorithms

Image segmentation is a key technique in image processing and computer vision field. From the point of view of geo-processing and application of remote sensing images, this paper emphasizes the importance of image segmentation for information extraction and targets recognition from remote sensing images and sets a classification system of common remote sensing image segmentation methods. In addition, this paper states the thoughts of high resolution RS image segmentation methods evaluation and tests it by evaluating four typical image segmentation algorithms based on features with six images qualitatively and quantitatively. The four typical image segmentation algorithms are Max-Entropy (ME), Split&Merge (SM), improved Gauss Markov Random Field(GMRF) and Orientation&Phase(OP). In the qualitative evaluation, this paper analyses these algorithms in terms of their rationale and gets a rough evaluation. In the quantitative evaluation, image complexity is taken into account firstly and five measures are employed. The five measures are removed region rumber, non uniformity within region measure, contrast across region measure, variance contrast across region measure and edge gradient measure. The qualitatively and quantitatively evaluation results are important to perform the optimal selection of segmentation algorithm in practical work. In the end, this paper draws some conclusions about high resolution remote sensing image segmentation and enumerates the flaws of image segmentation methods evaluation, especially it concludes the application prospect of high resolution RS image segmentation.