双时相影像联合不确定性对变化检测精度的影响机理探索

doi:10.3969/j.issn.0255-8297.2020.06.008

Abstract

Abstract: This paper aims at studying the influence of image uncertainty on change detection accuracy, and revealing a theoretical basis for improving change detection accuracy by suppressing uncertainty. Firstly, joint entropy is used to evaluate the joint uncertainty of a two-phase image. Then, based on spatial statistical correlation method, the relationship between the joint uncertainty and the indexes characterizing the change detection results' accuracy of the two-phase image is studied. Finally, according to the relationship, an effect model about the joint uncertainty and the accuracy of change detection of the two-phase image is established. Experimental results show that the joint uncertainty of bio-images performs a strong negative-correlation with the accuracy of change detection results in an influence mode of linear feature.

Key words: remote sensing images, uncertainty, change detection, influence mechanism

CLC Number:

P751.1

CHAO Jian, ZHANG Huifang, XU Changjun, ZHANG Penglin. Research on Influence Mechanism of Joint Uncertainty of Bio-images on Change Detection Accuracy[J]. Journal of Applied Sciences, 2020, 38(6): 916-923.

References

[1] 史文中, 张鹏林. 光学遥感影像变化检测研究的回顾与展望[J]. 武汉大学学报(信息科学版), 2018, 43(12):79-84. Shi W Z, Zhang P L. State-of-the-art remotely sensed images-based change detection methods[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):79-84. (in Chinese)
[2] Stein A, Yong G, Fabris R I. Introduction to the special issue "uncertainty in remote sensing image analysis"[J]. Remote Sensing, 2018, 10(12):1975.
[3] Guo J, Du S, Huo H, et al. Modeling the spectral uncertainty of geographic features in highresolution remote sensing images:semi-supervising and weighted interval type-2 fuzzy C-means clustering[J]. Remote Sensing, 2019, 11(15):1750.
[4] Zhang Q, Zhang P. An uncertainty descriptor for quantitative measurement of the uncertainty of remote sensing images[J]. Remote Sensing, 2019, 11(13):1560.
[5] Zhao X, Stein A, Chen X. Application of random sets to model uncertainties of natural entities extracted from remote sensing images[J]. Stochastic Environmental Research & Risk Assessment, 2010, 24(5):713-723.
[6] Zhang Q, Zhang P, Xiao Y. A modeling and measurement approach for the uncertainty of features extracted from remote sensing images[J]. Remote Sensing, 2019, 11(16):1841.
[7] 易维, 曾湧, 王凤阁, 等. 多源遥感影像提取火烧迹地面积不确定性研究[J]. 电子测量技术, 2018(9):91-94. Yi W, Zeng Y, Wang F G, et al. Uncertainty study of fire scar area extraction with different image sources[J]. Electronic Measurement Technology, 2018(9):91-94. (in Chinese)
[8] 竞霞, 魏曼, 王纪华, 等. 基于边界域修正粗糙熵模型的遥感影像分类不确定性评价[J]. 中国农业科学,2014, 47(11):2135-2141. Jing X, Wei M, Wang J H, et al. Uncertainty research of remote sensing image classification using the boundary region-based modified rough entropy model[J]. Scientia Agricultura Sinica, 2014, 47(11):2135-2141. (in Chinese)
[9] 黄恩兴. 遥感影像分类结果的不确定性研究[J]. 中国农学通报, 2010, 26(5):322-325. Huang E X. Research on classification uncertainties of remote sensing image[J]. Chinese Agricultural Science Bulletin, 2010, 26(5):322-325. (in Chinese)
[10] 张采芳, 田岩, 张荟平. 基于类半径不确定性度量的遥感影像分类[J]. 遥感信息, 2015, 30(3):111-115. Zhang C F, Tian Y, Zhang H P. Remote sensing image classification based on uncertainty measure of radius of category[J]. Remote Sensing Information, 2015, 30(3):111-115. (in Chinese)
[11] 林琳. 基于像元的遥感影像分类不确定性评价[J]. 科技信息, 2012(3):99, 69. Lin L. Uncertainty evaluation of remote sensing image classification based on pixel[J]. Science & Technology Information, 2012(3):99, 69. (in Chinese)
[12] Lan Z, Liu Y, Tang X, et al. Description and evaluation approach for uncertainty of RS images classification[J]. Geo-spatial Information Science, 2009, 12(1):72-78.
[13] 田园, 张雪芹, 孙瑞. 基于多源、多时相遥感影像的高原湖泊提取及其不确定性——以西藏羊卓雍错流域为例[J]. 冰川冻土, 2012, 34(3):563-572. Tian Y, Zhang X Q, Sun R. Extracting alpine lake information based on multi-source and multi-temporal satellite images and its uncertainty analysis-a case study in Yamzhog Yumco Basin, south Tibet[J]. Journal of Glaciology and Geocryology, 2012, 34(3):563-572. (in Chinese)
[14] Lewis H G, Brown M, Tatnall A R L. Incorporating uncertainty in land cover classification from remote sensing imagery[J]. Advances in Space Research, 2000, 26(7):1123-1126.
[15] Luisa M S G, Cidalia C F, Eduardo N B S J, et al. A method to incorporate uncertainty in the classification of remote sensing images[J]. International Journal of Remote Sensing, 2009, 30(20):5489-5503.
[16] Hao M, Shi W, Zhang H, et al. A scale-driven change detection method incorporating uncertainty analysis for remote sensing images[J]. Remote Sensing, 2016, 8(9):745.

Research on Influence Mechanism of Joint Uncertainty of Bio-images on Change Detection Accuracy

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 7

Recommended Articles

Metrics

Comments

[1]	LÜ Yan-xia, WANG Cui-rong, WANG Cong, YUAN Ying. Data Stream Classifcation with Data Uncertainty and Concept Drift [J]. Journal of Applied Sciences, 2017, 35(5): 559-569.
[2]	LIU Xue-jun, XU Hong-bing, DONG Yi-sheng, QIAN Jiang-bo, WANG Yong-li. Online Detection of Data Stream Changes Based on Maximum Frequent Itemset Entropy [J]. Journal of Applied Sciences, 2006, 24(5): 498-502.
[3]	DU Jia-lu;GUO Chen;YANG Cheng-en. Adaptive Nonlinear Design of Autopilot for Ship Course Tracking [J]. Journal of Applied Sciences, 2006, 24(1): 83-0083 .
[4]	LIANG Zheng. Guaranteed Cost H^∞ Control for a Class of Uncertain Discrete Time-Delay Systems [J]. Journal of Applied Sciences, 2004, 22(4): 545-548.
[5]	CHEN Yu-dong, SHI Song-jiao, WENG Zheng-xin. The Faulty Systems State Observer and Its Application to Fault Diagnosis in a Class of Nonlinear Systems [J]. Journal of Applied Sciences, 2002, 20(2): 153-159.
[6]	XIAO Wei-yue. A Study on the Uncertainty lnference in Fuzzy Rule [J]. Journal of Applied Sciences, 2002, 20(1): 94-98.
[7]	WANG XIAOGANG, QTAN WENHAN. TWO-LAYER ARCHITECTURE AND STRATEGIES FOR ROBITIC MULTISENSOR DATA FUSION [J]. Journal of Applied Sciences, 1995, 13(4): 439-446.