高空间分辨率遥感影像分割尺度参数自动选择研究

doi:10.3724/SP.J.1047.2016.00639

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 639-648.doi: 10.3724/SP.J.1047.2016.00639

高空间分辨率遥感影像分割尺度参数自动选择研究

王志华^1,²(), 孟樊¹, 杨晓梅^1,^*(), 杨丰硕^1,², 方豫³

1. 中国科学院地理科学与资源研究所,北京 100101
2. 中国科学院大学,北京 100049
3. 江西省遥感信息系统中心,南昌

收稿日期:2015-12-23 修回日期:2016-02-01 出版日期:2016-05-10 发布日期:2016-05-10
通讯作者: 杨晓梅 E-mail:zhwang@lreis.ac.cn;yangxm@lreis.ac.cn
作者简介:
作者简介：王志华（1988-）,男,河南信阳人,博士生,研究方向为高分辨率遥感地学计算、图像分割与评价、变化监测。E-mail:zhwang@lreis.ac.cn
基金资助:
国家科技支撑计划项目（2014BAL01B01）

Study on the Automatic Selection of Segmentation Scale Parameters for High Spatial Resolution Remote Sensing Images

WANG Zhihua^1,²(), MENG Fan¹, YANG Xiaomei^1,^*(), YANG Fengshuo^1,², FANG Yu³

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Center for Remote Sensing, GIS Application of Jiangxi, Nanchang 330046, China

Received:2015-12-23 Revised:2016-02-01 Online:2016-05-10 Published:2016-05-10
Contact: YANG Xiaomei E-mail:zhwang@lreis.ac.cn;yangxm@lreis.ac.cn

摘要/Abstract

摘要：

面向对象解译技术在高分辨率遥感影像信息提取中得到广泛应用,但影像分割的基础问题仍严重制约其自动化水平,尤其是分割参数选择。因此,本文以广泛使用的分型网络演化分割算法为例,开展尺度参数选择研究。借鉴对遥感影像分辨率敏感的局部方差指标,引入边长和面积权重,构造加权局部方差（WLV）指标,对多个分割结果进行评价,进而实现最佳尺度参数选择。在珠江区域2.5 m的SPOT 5融合影像上进行实验,通过计算最佳分割结果与人工分割结果的相似度对WLV进行定量验证。此外,还对WLV在分割对象最小为一个像元、最大为整景影像的全范围尺度参数的变化规律进行了实验,结果表明：在WLV随尺度参数的变化曲线中,不同极大值点的分割结果反映了实验区不同景观层级上的斑块,其中第1个极大值点对应的分割结果能够较好地反映影像的最小可识别单元。

关键词: 高分辨率遥感, 多尺度分割, 尺度参数选择, 图像分割评价, 加权局部方差

Abstract:

Geographic Object-Based Image Analysis (GEOBIA) is widely used in high spatial resolution remote sensing image interpretation. However, the fundamental component of image segmentation severely obstructs its development, especially on the selection of segmentation parameters. To overcome these issues, we choose the widely used Fractal Network Evolution Algorithm as an example, which is provided by eCognition, and focus on looking for an approach for the scale parameter selection. Inspired by the similarity between the merging segmentation and the degrading image resolution that the unit size within both would increase, we introduce the weights of border length and the weights of object area into the metric of Local Variance proposed by Woodcock and Strahler (1987), and propose a new segmentation evaluation metric: Weighted Local Variance (WLV). Through comparing WLV with a supervised metric on a series of segmentations with limited increasing scale parameters, we found that the best segmentation result chosen by the first local maximum point of the scale-WLV curve is similar to the manual segmentation result. Then we validate WLV on two more images and expand the limited scale space to the full range, so that the segments can change from one pixel to the whole image. Results show that the segmentations chosen by WLV local maximum points could reflect the different levels in the hierarchical landscape, and the segmentation of the first levels is capable of expressing the finest homogeneous patches.

Key words: high spatial resolution remote sensing images, multiscale segmentation, scale parameter selection, image segmentation evaluation, weighted local variance

王志华, 孟樊, 杨晓梅, 杨丰硕, 方豫. 高空间分辨率遥感影像分割尺度参数自动选择研究[J]. 地球信息科学学报, 2016, 18(5): 639-648.DOI:10.3724/SP.J.1047.2016.00639

WANG Zhihua,MENG Fan,YANG Xiaomei,YANG Fengshuo,FANG Yu. Study on the Automatic Selection of Segmentation Scale Parameters for High Spatial Resolution Remote Sensing Images[J]. Journal of Geo-information Science, 2016, 18(5): 639-648.DOI:10.3724/SP.J.1047.2016.00639

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高空间分辨率遥感影像分割尺度参数自动选择研究

Study on the Automatic Selection of Segmentation Scale Parameters for High Spatial Resolution Remote Sensing Images

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