Change Detection Over Sokolov Open Pit Mine Areas, Czech Republic, Using Multi Temporal HyMAP Data ( )

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1 Change Detection Over Sokolov Open Pit Mine Areas, Czech Republic, Using Multi Temporal HyMAP Data ( ) S. Adar* a G. Notesco b, A. Brook b, I. Livne b, P. Rojik c, V. Kopackova d, K. Zelenkova d, J. Mišurec d, A. Bourguignon e, S. Chevrel e, C. Ehrler f, C. Fisher f, J. Hanuš g, Y. Shkolnisky h and E. Ben Dor b a Porter School of Environmental Studies at Tel Aviv University, Tel Aviv, Israel; b Department of Geography and Human Environment, Tel Aviv University, Israel; c Sokolovská uhelná a. s., Staré nám. 69, Sokolov, Czech Republic; d Czech Geological Survey, Klárov 3, , Prague 1, Czech Republic; e BRGM, Mineral Resource division Orleans, France; f German Aerospace Center, German Remote Sensing Data Center, Dept. Land Applications, Wessling, Germany; g Institute of Systems Biology and Ecology of the Academy of Sciences of the Czech Republic, Na Sádkách 7, České Budějovice, Czech Republic; h Department of Applied Mathematics, Tel Aviv University, Israel; 1

2 Part of EO Miners FP7 Integrate new and existing Earth Observation tools to improve best practice in mining activities and to reduce the mining related environmental and societal footprint 2

3 Test sites Locations of the project Czech Republic Kyrgyzstan South Africa 3

4 Outline Research purpose and problem description Research procedure strategy Change detection (CD) algorithms description Results Conclusions

5 Research purpose Develop the most suitable change detection framework using HS data related to mining activities Implement, compare, customize and improve the known state of the art algorithms for classifying changes by using reflectance spectroscopy and Imaging spectroscopy Mapping the environmental changes in mining area in general and Sokolov area as a case study. 5

6 Hyperspectral Imaging Concept

7 Problem description Mining controlling authorities Field chemical measurements Area is too big 7

8 Mining area related types of changes Active Mine area Material/ mineral pile transfer Exposure of underground layers Active waste dumps Detect new waste dumps Detect changes/ alteration in waste dump mineral composition Abounded and re cultivated mines Health of vegetation Soil characteristics which affect vegetation survival possibilities Water quality in artificial lakes Concentration changes of pollutant indicators such as Jarosite and pyrite. Natural water bodies Extracted material transportation routes monitoring Manmade changes Includes changes such as new buildings, roads, relocation of portable structures etc. Land use land cover changes Urban Re cultivation waste dump Active mine Coal Abounded mine Railroad 8

9 Recent years research Different sensors High resolution (HR), Multispectral (MS), Hyperspectral HSR, Thermal infra red (TIR), Synthetic aperture radar (SAR) sensors to provide important environmental characteristics over mining areas Different methods These research activities focused on classification and regression mapping of the land and lack change detection (CD) classification over mining areas Most CD research activities have been focused on: MS spaceborne data Ground HS data Lack of research activities on Airborne HS data in mining area We focus on the CD of Airborne HS sensors in mining areas in this research 9

10 Sensors Research done with different sensors Non Mining application fields Classification /Regression Change Detection Mining Application field Classification /Regression Change Detection MS xx xx xx xx HR xx xx xx xx IS Ground (VIS) x x IS Airborne xx x xx + Amount of articles: XX = Many, X = Considerable, + = Few or none 10

11 Some field work 11

12 Research Procedure Strategy Stage1: Implement and apply CD algorithms on a small subset Stage2: Choose the most suitable approach and improve the algorithm

13 Sokolov coal mine area Mosaic image HyMap 2009 HyMap 2010

14 Stage1: Considerations in choosing CD algorithms Relatively simple and low complexity No dynamic programming (and no iterations) No learning procedures Common in the literature

15 Change detection algorithms 15

16 CD techniques Some of the most common algoritms Chronchrome is actually wiener s filter Covariance Equalization PCA principal component analysis SAM Spectral Angle Mapper KARMONMARIE VONGSY. CHANGE DETECTION METHODS FOR HYPERSPECTRAL IMAGERY 16

17 Change detection approaches Group Examples Advantages Disadvantages Algebraic methods Differencing, Rationing, Indexing Simple Very sensitive to registration atmospheric corrections errors, Transformation PCA, KT, GS, Chi squared Reduces data dimensionality, properties of change are highlighted Different scene characteristics have different performance Land cover change (Byrne et al. 1980, Classification/ Clustering Postclassification comparison, ANN Robust to atmospheric corr. & sensor errors, Nonlinear capabilities Highly depended on classification quality, requires large amount of training samples Over fitting problems, large amount of samples (Ward et al. 2000) Spectral unmixing Fractions have biophysical meaning Complex, emphasis on endmember retrieval (Adams et al. 1995, Roberts et al. 1998), 17

18 Chronochrome (CC) y image at time 1 x image at time 2 Two parts for the CD algorithm A linear predictor from x to y Assessment of the prediction error as an indicator for the change E error matrix tr(e) is the mean square error (MSE) The optimal L transformation in terms of square error is: Applying the RX detector for on the error image to detect changes 18 Schaum, A., and A. Stocker Long interval chronochrome target detection. In Proceedings of the International Symposium on Spectral Sensing Research.

19 Chronochrome (CC) y image at time 1 x image at time 2 Two parts for the CD algorithm A linear predictor from x to y Assessment of the prediction error as an indicator for the change E error matrix is the covariance of The optimal L transformation in terms of Min Mean Square Error (MMSE) is: Applying the RX detector on the error image to detect changes 19 Schaum, A., and A. Stocker Long interval chronochrome target detection. In Proceedings of the International Symposium on Spectral Sensing Research.

20 Covariance Equalization (CE) Although CC provides optimal linear transformation, it requires no registration error CE transforms image covariance properties to the other, it is suboptimal but resistant to registration error Criteria: Schaum, A., and A. Stocker. 2004a.Advanced algorithms for autonomous hyperspectral change detection. In Applied Imagery Pattern Recognition Workshop, Proceedings. 33rd, doi: /aipr

21 PCA based CD Retrieve the principal components from the covariance matrix Change detection as a clustering problem Wiemker, R., A. Speck, D. Kulbach, H. Spitzer, and J. Bienlein Unsupervised robust change detection on multispectral imagery using spectral and spatial features. In Proceedings of the Third International Airborne Remote Sensing Conference and Exhibition, 1:

22 Spectral Angle Mapper (SAM) x y High value = Significant change Medium value = Medium change High value = Significant change Low value = No change

23 Results 23

24 Sokolov coal mine area 2009 and 2010 HyMap data Coal mine area progress in one year

25 CD algorithms results

26 CD algorithms results Not detected Detected Not detected Detected

27 How well does the linear transformation works (LCC)? 27

28 Other CD results

29 Conclusions so far Every algorithm shows something that is a bit different The linear transformation does not perform very well and needs to modified Statistical approaches assume that most of the image did not change which is not necessarily true The algorithms can detect changes that are not visible to the naked eye We are going to use SAM as a starting point for unsupervised CD We need a better way to present the changes in a wide area 29

30 Thank you for your attention