Noise Estimation from Remote Sensing Images by Fractal Theory and Adaptive Image Block Division

doi:10.11947/j.AGCS.2015.20140327

Acta Geodaetica et Cartographica Sinica ›› 2015, Vol. 44 ›› Issue (11): 1235-1245.doi: 10.11947/j.AGCS.2015.20140327

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Noise Estimation from Remote Sensing Images by Fractal Theory and Adaptive Image Block Division

FU Peng^1,2, SUN Quansen¹, JI Zexuan¹

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;
2. School of Information Technologies, University of Sydney, Sydney 2006, AustraliaAbstract

Received:2014-06-19 Revised:2014-10-19 Online:2015-11-20 Published:2015-11-25
Supported by:
The National Natural Science Foundation of China(Nos.6127325161401209) China Postdoctoral Science Foundation(Nos.2014T705252013M531364) Project of Civil Space Technology Pre-research of the 12th Five-year plan(No.D040201) Natural Science Foundation of Jiangsu Province of China(Youth Fund Project)(No.BK20140790) Scientific Research and Innovation Project Fund for Graduate Students of Jiangsu Province Higher Education Institution(No.CX22B_0211)

Abstract

Abstract: A novel approach for additive noise estimation from highly textured optical remote sensing images has been proposed, which is based on fractal theory and adaptive image block division. Different from the conventional regular block division based noise estimation methods, the divided adaptive image blocks with the proposed method are adhering to the local image information, which are most likely to be homogeneous blocks. Combining with the week textured image region detection using fractal theory and noise standard deviation calculation using statistical analysis, the proposed method can automatically estimate additive noise intensity from optical remote sensing images. Quantified analysis of experiments with ZY-3 satellite images demonstrates that the proposed method is applicable to optical remote sensing images with various complexities and different noise levels. Meanwhile, the notion of week textured image region detection and adaptive image block division can also be applied to multiplicative noise estimation from radar images after modification.

Key words: optical remote sensing images, noise estimation, fractal theory, texture analysis, adaptive image block, radar images

CLC Number:

P237

FU Peng, SUN Quansen, JI Zexuan. Noise Estimation from Remote Sensing Images by Fractal Theory and Adaptive Image Block Division[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11): 1235-1245.

References

[1] WATANABE J, HOSAKA T, HAMAMOTO T. Sensor-Pattern-Noise Map Reconstruction in Source Camera Identification for Size-reduced Images[J]. IEICE Transactions on Information and Systems, 2013, 96(8):1882-1885.
[2] TAO Ke, ZHU Jianjun. A Hybrid Indicator for Determining the Best Decomposition Scale of Wavelet De-noising[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(5):749-755.(陶珂, 朱建军. 多指标融合的小波去噪最佳分解尺度选择方法[J]. 测绘学报, 2012, 41(5):749-755.)
[3] SHARON A, MEIRAV G, ACHI B, et al. Image Segmentation by Probabilistic Bottom-up Aggregation and Cue Integration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(2):315-327.
[4] YANG Honglei, PENG Junhuan, LI Shuhui, et al. Log-principal Component Transformation Based EM Algorithm for Remote Sensing Classification[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4):378-382.(杨红磊, 彭军还, 李淑慧, 等. 基于对数-主成分变换的EM算法用于遥感影像分类[J]. 测绘学报, 2010, 39(4):378-382.)
[5] IWEN M A, TEWFIK A H. Adaptive Strategies for Target Detection and Localization in Noisy Environments[J]. IEEE Transactions on Signal Processing, 2012, 60(5):2344-2353.
[6] GAO B C. An Operational Method for Estimating Signal to Noise Ratios from Data Acquired with Imaging Spectrometers[J]. Remote Sensing of Environment, 1993, 43(1):23-33.
[7] CORNER B R, NARAYANAN R M, REICHENBACH S E. Noise Estimation in Remote Sensing Imagery Using Data Masking[J]. International Journal of Remote Sensing, 2003, 24(4):689-702.
[8] LEE J S, HOPPEL K. Noise Modeling and Estimation of Remotely Sensed Images[C]//IEEE International Geoscience and Remote Sensing Symposium.Vancouver:IEEE, 1989:1005-1008.
[9] LEE J S, HOPPEL K, MANGO S A. Unsupervised Estimation of Speckle Noise in Radar Images[J]. International Journal of Imaging Systems and Technology, 1992, 4(4):298-305.
[10] XU Bin, CUI Yi, ZHOU Guangyi, et al. Unsupervised Speckle Level Estimation of SAR Images Using Texture Analysis and AR Model[J]. IEICE Transactions on Communications, 2014, E97-B(3):691-698.
[11] IMMERKER J. Fast Noise Variance Estimation[J]. Computer Vision and Image Understanding, 1996, 64(2):300-302.
[12] LIU Wei, LIN Weisi. Additive White Gaussian Noise Level Estimation in SVD Domain for Images[J]. IEEE Transactions on Image Processing, 2013, 22(3):872-883.
[13] HASHEMI M, BEHESHTI S. Adaptive Noise Variance Estimation in Bayes Shrink[J]. IEEE Signal Processing Letters, 2010, 17(1):12-15.
[14] AMER A, DUBOIS E. Fast and Reliable Structure-oriented Video Noise Estimation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2005, 15(1):113-118.
[15] FU Peng, SUN Quansen, JI Zexuan, et al. A New Method for Noise Estimation in Single-band Remote Sensing Images[C]//IEEE International Conference on Fuzzy Systems and Knowledge Discovery.Chongqing:IEEE, 2012:1664-1668.
[16] FU Peng, SUN Quansen, JI Zexuan, et al. A Method of SNR Estimation and Comparison for Remote Sensing Images[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4):559-567.(傅鹏, 孙权森, 纪则轩, 等. 一种遥感图像信噪比评估和度量准则[J]. 测绘学报, 2013, 42(4):559-567.)
[17] LIU Xinhao, TANAKA M, OKUTOMI M. Noise Level Estimation Using Weak Textured Patches of a Single Noisy Images[C]//IEEE International Conference on Image Processing.Orlando:IEEE, 2012:665-668.
[18] LIU Xinhao, TANAKA M, OKUTOMI M. Single-image Noise Level Estimation for Blind Denoising[J]. IEEE Transactions on Image Processing, 2013, 22(12):5226-5237.
[19] QIN Bangyong, HONG Bo, ZHANG Zhi, et al. A Generally Applicable Noise-Estimating Method for Remote Sensing Images[J]. Remote Sensing Letters, 2014, 5(5):481-490.
[20] SILVETTI A F, DELRIEUX C A. Quadratic Self-correlation:An Improved Method for Computing Local Fractal Dimension in Remote Sensing Imagery[J]. Computers & Geosciences, 2013, 60:142-155.
[21] SUN W, XU G, GONG P, et al. Fractal Analysis of Remotely Sensed Images:A Review of Methods and Applications[J]. International Journal of Remote Sensing, 2006, 27(22):4963-4990.
[22] PENTLAND A P. Fractal-based Description of Natural Scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, PAMI-6(6):661-674.
[23] SARKAR N, CHAUDHURI B B. An Efficient Approach to Estimate Fractal Dimension of Textured Images[J]. Pattern Recognition, 1992, 25(9):1035-1041.
[24] ACHANTA R, SHAJI A, SMITH K, et al. SLIC Superpixels Compared to State-of-the-art Superpixel Methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2274-2281.
[25] XIANG Deliang, TANG Tao, ZHAO Lingjun, et al. Superpixel Generating Algorithm Based on Pixel Intensity and Location Similarity for SAR Image Classification[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6):1414-1418.
[26] AIAZZI B, ALPARONE L, BARONTI S, et al. Coherence Estimation from Multilook Incoherent SAR Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(11):2531-2539.

Noise Estimation from Remote Sensing Images by Fractal Theory and Adaptive Image Block Division

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