• 本期要文(可全文下载) •

### 高分辨率影像分割的分形网络演化改进方法

1. 中国科学院遥感与数字地球研究所 遥感科学国家重点实验室, 北京 100101
• 收稿日期:2013-03-29 修回日期:2013-04-28 出版日期:2014-01-05 发布日期:2014-01-05
• 作者简介:邓富亮（1982-），男，博士生，主要从事遥感图像处理和应用研究。E-mail：fldeng8266@gmail.com
• 基金资助:

高分辨率对地观测系统重大专项（02-Y30A04-9001-12/13）。

### An Improved Method of FNEA for High Resolution Remote Sensing Image Segmentation

DENG Fuliang, YANG Chongjun, CAO Chunxiang, FAN Xieyu

1. State Key laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, CAS, Beijing 100101, China
• Received:2013-03-29 Revised:2013-04-28 Online:2014-01-05 Published:2014-01-05

Abstract:

Fractal Net Evolution Approach (FNEA) is a high precision algorithm for high resolution remote sensing image segmentation. The segmentation algorithm starts with single image objects of one pixel and repeatedly merges them in several loops in pairs to larger units as long as an upper threshold of homogeneity is not exceeded locally. However, it's high computational cost for large images such as the currently high resolution remote sensing images includes two aspects, one is the time-consuming procedure of creation of initial image objects, and the other is the great amount of initial image objects. In order to boost the speed of the algorithm, generally, an effective improvement measure is to select a faster segmentation method to generate the initial image objects, and then carry out the region merging phase. Thus, the main focus of this paper is to tackle this problem by using a parallel process to segment the original image into subsets as the initial image objects firstly. For the original image data preprocessing, a regular data division way is used to divide the original image data into sub-rectangular data blocks, which will be used as the input data and assigned to different threads for the parallel computing. We introduced an improved method of fractal net evolution approach, the main work as follows: (i) Presenting an automatic seed selection method. The initial seeds are automatically selected from the original image data and the seeded image objects are produced where each image object corresponds to a seed. (ii) Proposing a parallel region growing strategy upon the data paralleled segmentation. Moreover, the strategy solves the problem of merging image objects on both sides of the dividing lines as well. (iii) Using the OpenMP parallel technology. Experimental results, including comparison of final segmentation and assessment of computing efficiency, show that the improved method is more effective and the final segmentation result is reproducible. Thus, the generality and reliability of the method proved the practical value of our work.