High-resolution remote sensing image segmentation based on weight adaptive fractal net evolution approach

doi:10.6046/gtzyyg.2013.04.04

Abstract
References
Related Articles
Metrics

Download: PDF(713 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In the methods for high-resolution remote sense image segmentation,the fractal net evolution approach (FNEA)is relatively mature among the object-oriented image segmentation algorithms.In calculating the heterogeneity of each pair of neighboring objects,the spatial criterion and weight of compactness are user-defined according to experience. In this paper,an improved method of adaptive FNEA algorithm was proposed by adaptively calculating the weights of spatial criterion and compactness according to the different properties of various kinds of objects. Moreover,the contributions of different spectroscopic components were introduced into calculating of the heterogeneity. Computer simulation demonstrates that the proposed algorithm has better adaptability to the image objects with different attributes. A comparison with some similar algorithms shows that the method proposed in this paper performs better for image segmentation.

Keywords Beijing wetland functional assessment zoning remote sensing GIS

:	TP751.1
	TP311

Issue Date: 21 October 2013

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	MIAO Lili
	JIANG Weiguo
	WANG Shidong
	ZHU Lin

Cite this article:

MIAO Lili,JIANG Weiguo,WANG Shidong, et al. High-resolution remote sensing image segmentation based on weight adaptive fractal net evolution approach[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 22-25.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.04.04 OR https://www.gtzyyg.com/EN/Y2013/V25/I4/22

[1] 王文杰,赵忠明,朱海青.面向对象特征融合的高分辨率遥感图像变化检测方法[J].计算机应用研究,2009,26(8):3149-3151. Wang W J,Zhao Z M,Zhu H Q.Object-oriented change detection method based on features fusion for high-resolution remote sensing image[J].Application Research of Computers,2009,26(8):3149-3151.
[2] Rizvi I A,Mohan B K.Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process[J].IEEE Transaction on Geoscience and Remote Sensing,2011,49(12):4815-4820.
[3] 卢昭羿,左小清,黄亮,等.面向对象的投影互分割道路变化检测[J].国土资源遥感,2012,24(3): 60-64. Lu Z Y,Zuo X Q,Huang L,et al.Road change detection using object-oriented projective interactive partition[J].Remote Sensing for Land and Resources,2012,24(3):60-64.
[4] 沈金祥,杨辽,陈曦,等.面向对象的山区湖泊信息自动提取方法[J].国土资源遥感,2012,24(3):84-91. Shen J X,Yang L,Chen X,et al.A method for object-oriented automatic extraction of lakes in the mountain area from remote sensing image[J].Remote Sensing for Land and Resources,2012,24(3):84-91.
[5] 徐宏根,王建超,郑雄伟,等.面向对象的植被与建筑物重叠区域的点云分类方法[J].国土资源遥感,2012,24(2):23-27. Xu H G,Wang J C,Zheng X W,et al.Object-based point clouds classification of the vegetation and building overlapped area[J].Remote Sensing for Land and Resources,2012,24(2):23-27.
[6] Baatz M,Schape A.Multiresolution segmentation:An optimization approach for high quality multiscale image segmentation[C]//Heidelberg:Angewandte Geographische Information Sverarbeitung XII.2000:12-23
[7] 高伟,刘修国,彭攀,等.一种改进的高分辨率遥感影像分割方法[J].中国地质大学学报,2010,25(3):421-425. Gao W,Liu X G,Peng P,et al.An improved method of high resolution remote sensing image segmentation[J].Earth Science—Journal of China University of Geosciences,2010,25(3):421-425.
[8] Tang Y Q,Zhang L P,Huang X.Object-oriented change detection based on the Kolmogorov-Smirnov test using high-resolution multispectral imagery[J].International Journal of Remote Sensing,2011,32(20):5719-5740.
[9] 王慕华,张继贤,李海涛,等.基于区域特征的高分辨率遥感影像变化检测研究[J].测绘科学,2009,34(1):92-94. Wang M H,Zhang J X,LI H T,et al.High resolution image change detection based on region feature[J].Science of Surveying and Mapping,2009,34(1):92-94.

[1]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2]	LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[3]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[4]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[5]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[6]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[7]	ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[8]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[9]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[10]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[11]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[12]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[13]	WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[14]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[15]	YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.

Viewed

Full text

Abstract

Cited

Shared

Discussed