Dual-thresholds change detection in GF-3 SAR images

Bin CUI; Yonghong ZHANG; Li YAN; Jujie Wei

doi:10.11834/jrs.20208179

Chinese Satellites | Views : 0 下载量: 116 CSCD: 4 更多指标

PDF
Export
Share
Collection
Album

Dual-thresholds change detection in GF-3 SAR images
Vol. 24, Issue 1, Pages: 1-10(2020)
Published： 07 January 2020 ，
DOI： 10.11834/jrs.20208179

扫描看全文

崔斌,张永红,闫利,魏钜杰.2020.高分三号SAR影像双阈值变化检测.遥感学报,24(1): 1-10Cui B,Zhang Y H,Yan L and Wei J J. 2020. Dual-thresholds change detection in GF-3 SAR images. Journal of Remote Sensing(Chinese), 24(1): 1-10[DOI：10.11834/jrs.20208179]

CUI Bin,ZHANG Yonghong,YAN Li,Wei Jujie. 2020. Dual-thresholds change detection in GF-3 SAR images. Journal of Remote Sensing(Chinese). 24(1): 1-10
崔斌,张永红,闫利,魏钜杰.2020.高分三号SAR影像双阈值变化检测.遥感学报,24(1): 1-10Cui B,Zhang Y H,Yan L and Wei J J. 2020. Dual-thresholds change detection in GF-3 SAR images. Journal of Remote Sensing(Chinese), 24(1): 1-10[DOI：10.11834/jrs.20208179] DOI：

CUI Bin,ZHANG Yonghong,YAN Li,Wei Jujie. 2020. Dual-thresholds change detection in GF-3 SAR images. Journal of Remote Sensing(Chinese). 24(1): 1-10 DOI： 10.11834/jrs.20208179.

摘要

双阈值合成孔径雷达SAR(Synthetic Aperture Radar)变化检测算法具有在发现变化区域的同时还能确定地表发生后向散射变化类型的优点。针对广义高斯双阈值最小误差法D-GKIT(Dual Generalized Kittler and Illingworth Thresholding)在进行阈值选取时直方图中不同类别像素灰度级重叠严重时，分割结果容易在尖峰单侧选取出双阈值而导致无法正确分割差异图的问题，本文提出一种结合归一化最大类间方差和广义高斯最小误差法GKIT(Generalized Kittler and Illingworth Thresholding)的双阈值SAR变化检测方法。首先，提出以归一化最大类间方差值作为灰度级重叠程度的判别参数，确定阈值的选取顺序及两个候选区间；然后，利用GKIT在候选区间内进行分割，获取单侧阈值及非变化类拟合函数；最后，提出利用非变化类拟合函数更新后的直方图作为另一侧阈值选取基础进行分割，得到对应分割阈值。以宁波地区高分三号(GF-3)SAR卫星影像作为试验研究数据，结果表明：本文方法能较好地解决灰度级重叠时D-GKIT无法进行正确分割的问题，具有良好的变化检测效果和更强的鲁棒性且达到了利用研究区数据验证利用GF-3号SAR卫星影像进行变化检测研究可行性的目的。

Abstract

Compared with the single threshold segment method in SAR change detection

the dual thresholds segment method can simultaneously identify the change areas and confirm the change types. Although D-GKIT shows a superior performance

a strongly overlapping gray level is observed in the histogram of the difference image

thereby inaccurately identifying double thresholds on the same side of the peak. In this paper

we apply a dual-thresholds method combined with normalized maximal between-class variance and GKIT test on GF-3 images to verify its feasibility of our method and the ability of change detection ability.First

the normalized maximal between-class variance values of two sides surrounding the peak in the histogram are taken as the degrees of the overlapping gray level

and then the thresholds selection sequence and the candidate intervals are confirmed. Second

the side at which the gray level lightly overlaps is segmented by GKIT

and the threshold and the fitting function of the unchanged class are obtained. Third

the fitting function of the unchanged class is used to replace the corresponding part in the origin histogram to form a new histogram that is subsequently segmented to obtain the threshold in the second candidate interval. Finally

the two thresholds are applied on the difference image to obtain the final change result.

The experiment on GF-3 SAR images reveals that the performance on our proposed method outperforms D-GKIT and can deal well with the overlapping gray level overlapped in the histogram of the difference image. The confusion matrix of the results for various local areas in the change image also shows that the proposed method has been slightly influenced by the overlapping gray level overlapped and obtains generally good results. Therefore

the feasibility of our method and its change detection ability by using GF-3 images are verified.

We propose a method based on the normalized maximal between-class variance and GKIT to segment a difference image by applying dual thresholds in SAR change detection. The effectiveness of the proposed method and its change detection ability by using GF-3 images are validated by the experiment results.

关键词

遥感SAR变化检测双阈值分割高分三号(GF-3)GKIT归一化最大类间方差

Keywords

remote sensingSAR change detectiondual thresholds segmentGF-3GKITnormalized maximal between-class variance

references

Bazi Y, Bruzzone L and Melgani F. 2005. An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(4): 874-887 [DOI: 10.1109/TGRS.2004.842441http://dx.doi.org/10.1109/TGRS.2004.842441]

Bazi Y F, Bruzzone L and Melgani F. 2006. Automatic identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 3(3): 349-353 [DOI: 10.1109/LGRS.2006.869973http://dx.doi.org/10.1109/LGRS.2006.869973]

Demirkaya O and Asyali M H. 2004. Determination of image bimodality thresholds for different intensity distributions. Signal Processing: Image Communication, 19(6): 507-516 [DOI: 10.1016/j.image.2004.04.002http://dx.doi.org/10.1016/j.image.2004.04.002]

Gao C S, Zhang H, Wang C and Wu F. 2010. SAR change detection based on generalized Gamma distribution divergence and auto-threshold segmentation. Journal of Remote Sensing, 14(4): 710-724

高丛珊, 张红, 王超, 吴樊. 2010. 广义Gamma模型及自适应KI阈值分割的SAR图像变化检测. 遥感学报, 14(4): 710-724 [DOI: 10.11834/jrs.20100407http://dx.doi.org/10.11834/jrs.20100407]

Gong M G, Li Y, Jiao L C, Jia M and Su L Z. 2014. SAR change detection based on intensity and texture changes. ISPRS Journal of Photogrammetry and Remote Sensing, 93:123-135 [DOI: 10.1016/j.isprsjprs.2014.04.010http://dx.doi.org/10.1016/j.isprsjprs.2014.04.010]

Gong M G, Su L Z, Li H and Liu J. 2016. A survey on change detection in synthetic aperture radar imagery. Journal of Computer Research and Development, 53(1): 123-137

公茂果,苏临之, 李豪, 刘嘉. 2016. 合成孔径雷达影像变化检测研究进展. 计算机研究与发展, 53(1): 123-137 [DOI: 10.7544/issn1000-1239.2016.20150662http://dx.doi.org/10.7544/issn1000-1239.2016.20150662]

Hame T, Heiler I and Miguel-Ayanz J S. 1998. An unsupervised change detection and recognition system for forestry. International Journal of Remote Sensing, 19(6): 1079-1099 [DOI: 10.1080/014311698215612http://dx.doi.org/10.1080/014311698215612]

Hao H M, Zhang Y H, Shi H Y and Huang J B. 2012. Application of test statistic method in fully polarimtric SAR change detection. Journal of Remote Sensing, 16(3): 520-532

郝洪美, 张永红, 石海燕, 黄金波. 2012. 统计假设检验方法在全极化SAR变化检测中的应用. 遥感学报, 16(3): 520-532 [DOI: 10.11834/jrs.20120384http://dx.doi.org/10.11834/jrs.20120384]

Hu H T and Ban Y F. 2014. Unsupervised change detection in multitemporal SAR images over large urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8): 3248-3261 [DOI: 10.1109/JSTARS.2014.2344017http://dx.doi.org/10.1109/JSTARS.2014.2344017]

Hu Z L. 2013. An unsupervised change detection approach based on KI dual thresholds under the generalized gauss model assumption in SAR images. Acta Geodaetica et Cartographica Sinica, 42(1): 116-122

胡召玲. 2013. 广义高斯模型及KI双阈值法的SAR图像非监督变化检测. 测绘学报, 42(1): 116-122

Huang S Q, Liu D Z, Hu M X and Wang S C. 2010. Multi-temporal SAR image change detection technique based on wavelet transform. Acta Geodaetica et Cartographica Sinica, 39(2): 180-186

黄世奇, 刘代志, 胡明星, 王仕成. 2010. 基于小波变换的多时相SAR图像变化检测技术. 测绘学报, 39(2): 180-186

Kittler J and Illingworth J. 1986. Minimum error thresholding. Pattern Recognition, 19(1): 41-47 [DOI: 10.1016/0031-3203(86)90030-0http://dx.doi.org/10.1016/0031-3203(86)90030-0]

Ma J J, Gong M G and Zhou Z Q. 2012. Wavelet fusion on ratio images for change detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 9(6): 1122-1126 [DOI: 10.1109/LGRS.2012.2191387http://dx.doi.org/10.1109/LGRS.2012.2191387]

Moser G and Serpico S B. 2006. Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(10): 2972-2982 [DOI: 10.1109/TGRS.2006.876288http://dx.doi.org/10.1109/TGRS.2006.876288]

Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]

Pantze A, Santoro M and Fransson J E S. 2014. Change detection of boreal forest using bi-temporal ALOS PALSAR backscatter data. Remote Sensing of Environment, 155: 120-128 [DOI: 10.1016/j.rse.2013.08.050http://dx.doi.org/10.1016/j.rse.2013.08.050]

Tsai D M. 1995. A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16(6): 653-666 [DOI: 10.1016/0167-8655(95)80011-Hhttp://dx.doi.org/10.1016/0167-8655(95)80011-H]

Whittle M, Quegan S, Uryu Y, Stüewe M and Yulianto K. 2012. Detection of tropical deforestation using ALOS-PALSAR: a Sumatran case study. Remote Sensing of Environment, 124: 83-98 [DOI: 10.1016/j.rse.2012.04.027http://dx.doi.org/10.1016/j.rse.2012.04.027]

Xiong B L, Chen J M and Kuang G Y. 2012. A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images. Remote Sensing Letters, 3(3): 267-275 [DOI: 10.1080/01431161.2011.572093http://dx.doi.org/10.1080/01431161.2011.572093]

Yousif O and Ban Y F. 2014. Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarity weights. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10): 4288-4300 [DOI: 10.1109/JSTARS.2014.2347171http://dx.doi.org/10.1109/JSTARS.2014.2347171]

Zhang Q J. 2017. System design and key technologies of the GF-3 satellite. Acta Geodaetica et Cartographica Sinica, 46(3): 269-277

张庆君. 2017. 高分三号卫星总体设计与关键技术. 测绘学报, 46(3): 269-277 [DOI: 10.11947/j.AGCS.2017.20170049http://dx.doi.org/10.11947/j.AGCS.2017.20170049]

Zhang X Q, Xiong B L and Kuang G Y. 2015. A ship target discrimination method based on change detection in SAR imagery. Journal of Electronics and Information Technology, 37(1): 63-70

张小强, 熊博莅, 匡纲要. 2015. 一种基于变化检测技术的SAR图像舰船目标鉴别方法. 电子与信息学报, 37(1): 63-70 [DOI: 10.11999/JEIT140143http://dx.doi.org/10.11999/JEIT140143]

Alert me when the article has been cited

提交

Transparent earth-observing： Exploring the new generation of earth observation system

Transferring deep convolutional neural network models for generalization mapping of autumn crops

Grid A*：A fast path planning algorithm for air-ground cooperative response of emergency rescue in the field

Construction method of a Spartina alterniflora index based on hyperspectral satellite images

Development of the high-resolution， high-sensitivity， large-width thermal infrared camera for 5 m Optical 02 Satellite