臺灣博碩士論文加值系統

現今大多變遷偵測的方法只以單一像素為處理單位，但受限於衛星影像空間解析度，一個像素本身常包含一種以上的成份物質。為了從影像取得更精密的資訊，我們研究了一些光譜解混技術，如獨立成份分析法、非負矩陣分解法、非監督式完全限制最小平方法、和頂點成份分析法，並利用它們偵測亞像元等級的變遷。本篇論文即為一研究特例報告，利用亞像元變遷偵測應用於偵測崩塌地的變遷。我們先利用光譜解混技術將多光譜影像的豐度特徵莘取出來，再合併原有崩塌地地理上的特性(例如坡度這個特徵)，進行事後分類比較型的變遷辨識。實驗結果顯示，亞像元變遷偵測超越以往單一像素的變遷偵測，可以提供更多的資訊。

Most of change detection algorithms for multi-temporal images are performed in unit of pixels. Due to the limit of spatial resolution, a pixel is, in many cases, a mixed pixel that consists of more than one ground cover types. We reviewed several spectral unmixing techniques such as independent component analysis (ICA), non-negative matrix factorization (NMF), unsupervised fully constrained least squares linear unmixing (UFCLSLU) and vertex component analysis (VCA). We employed the spectral unmixing techniques to explore subpixel information and to detect subpixel-scale changes. Furthermore, we demonstrated an application of subpixel change detection to detection of landslide expansions. The abundance feature extracted from multispectral images by spectral unmixing was incorporated with the slope feature into the process of landslide change identification based on the post-classification comparison procedure. Our result shows that the subpixel change detection method can provide more detailed information about landslide changes than pixel-based change detection algorithms.

1. Introduction 1
1.1 Background and Motivation 1
1.2 Organization 4
2. Related Work 5
2.1 Linear mixing model 6
2.2 Independent Component Analysis (ICA) 6
2.3 Non-negative Matrix Factorization (NMF): 10
2.4 Unsupervised fully constrained least squares linear unmixing algorithm (UFCLSLU) 12
2.5 Vertex Component Analysis (VCA) 14
2.6 Change detection based on Simple Differencing 16
2.7 Change detection based on Significance and Hypothesis Tests 17
2.8 Change detection based on integrating spectral feature and texture feature 18
2.9 Change Identification by Post-classification Comparison 19
2.10 Adaptive Bayesian Contextual Classification Based on Markov Random Fields 19
3. Proposed Approaches 22
3.1 Subpixel Change Detection 25
3.2 Subpixel Change Identification 26
4. Experiments 28
4.1 Datasets 28
4.2 Simulated data 31
4.3 Real data 35
4.3.1 Comparison of Change Detection Methods 38
4.3.2 Comparison of Change Identification Methods 41
5. Conclusions 49
6. References 50

[1]L.-S. Liang, K.-S. Chen, C.-L. Wang, A. J. Chen and W.-M. Boerner, “Landslide monitoring and assessment in Taiwan using SPOT series satellites,” Proc. IEEE Conf. Geosci. Remote Sensing, vol. 2, pp. 25-29, July 2005.
[2]R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294-307, March 2005.
[3]L. Bruzzone and D. F. Prieto, “An adaptive semiparametric and context-based approach unsupervised change detection in multitemporal remote-sensing images,” IEEE Trans. Image Processing, vol. 11, no. 4, pp. 452-466, April 2002.
[4]L. D. Stefano, S. Mattoccia, and M. Mola, “A change-detection algorithm based on structure and color,” Proc. IEEE Conf. Advanced Video and Signal-Based Surveillance, pp. 252-259, July 2003.
[5]L. Li and M. K. H. Leung, “Integrating intensity and texture differences for robust change detection,” IEEE Trans. Image Processing, vol. 11, no. 2, pp. 105-112, February 2002.
[6]G. G. Hazel, “Object-level change detection in spectral imagery,” IEEE Trans. Geosci. Remote Sensing, vol. 3, no. 3, pp. 553-561, March 2001.
[7]E. D. Kolaczyk, “On the use of prior and posterior information in the subpixel proportion problem,” IEEE Trans. Geosci. Remote Sensing, vol. 39, no. 7, pp. 2687-2691, July 2001.
[8]D. Manolakis, C. Siracusa, and G. Shaw,”Hyperspectral subpixel target detection using the linear mixing mode,” IEEE Trans. Geosci. Remote Sensing, vol. 41, no. 11, pp. 1392-1409, November 2003.
[9]C.-I. Chang, H. Ren, C.-C. Chang, F. D’Amico, and J. O. Jensen, “Estimation of subpixel target size for remotely sensed imagery,” IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 6, pp.1309-1320, June 2004.
[10]N. Keshava, and J. F. Mustard, “Spectral unmixing”, IEEE Trans. Signal Processing Magazine, vol. 19, no. 1, pp.44-57, January 2002.
[11]G. Foody, R. Lucas, P. Curran, and M. Honzak, “Non-linear mixture modeling without end-members using an artificial neural network,” Int. J. Remote Sensing, vol. 18, no. 4, pp. 937-953, 1997.
[12]R. Defries, J. Townshend, and M. Hansen, “Continuous fields of vegetation characteristics at the global scale at 1-km resolution,” J. Geophys. Res, vol. 104, pp. 16911-16923, 1999.
[13]R. Schowengerdt, “On the estimation of spatial-spectral mixing with classifier likelihood functions,” Pattern Recognit. Lett, vol. 17, no. 13, pp. 1379-1387, 1996.
[14]D. A. Roberts, G. T. Batista, J. L. G. Pereira, E. K. Waller and B. W. Nelson, “Change identification using multitemporal spectral mixture analysis,” Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, pp.137-154.
[15]J. B. Adams, D. Sabol, V. Kapos, R. A. Filho, D. A. Roberts, M. O. Smith and A. R. Gillespie, “Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon,” Remote Sensing of Environment, vol. 52, pp. 137-154, 1995.
[16]J. M. Piwowar, D. R. Peddle and E. F. Ledrew, “Temporal mixture analysis of arctic sea ice imagery,” Remote Sensing of Environment, vol. 63, pp. 195-207, 1998.
[17]S. L. Ustin, D. A. Roberts and Q. J. Hart, “Seasonal vegetation patterns in a California coastal savanna derived from advanced visible/infrared imaging spectrometer(AVIRIS) Data,” Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, pp.163-180.
[18] D. Lu, P. Mausel, E. Brondizio and E. Moran, “Change detection techniques,” Int. J. Remote Sensing, vol. 25, no. 12, pp. 2365-2407, June 2004.
[19] P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin, “Digital change detection methods in ecosystem monitoring: a review,” Int. J. Remote Sensing, vol. 25,no. 9, pp. 1565-1596, 2004.
[20]J. Rogan, J Franklin and D. A. Roberts, ”A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery,” Remote Sensing of Environment, vol. 80, no. 1, pp. 143-156, 2002.
[21]V. C. Radeloff, D. J. Mladenoff and M. S. Boyce, “Detecting jack pine budworm defoliation using spectral mixture analysis:separating effects from determinants,” Remote Sensing of Environment, vol. 69, pp. 156-169, 1999.
[22]C. A. Mucher, K.T. Steinnocher, F. P. Kressler and C. Heunks, “Land cover characterization and change detection for environmental monitoring of pan Europe,” Int. J. Remote Sensing, vol. 21, no. 6, pp. 1159-1182, 2000.
[23]C. A. Wessman, C. A. Bateson and T. L. Benning, “Detecting fire and grazing patterns in tallgrass prairie using spectral mixture analysis,” Ecological Applications, vol. 7, no. 2, pp.493-511, 1997.
[24]V. Haertel, Y. E. Shimabukuro, and R. A. Filho, “Fraction images in multitemporal change detection,” Int. J. Remote Sensing, vol. 25, no. 23, pp. 5473-5489, 2004.
[25]C. Souza and P. Barreto, “An alternative approach for detecting and monitoring selectively logged forests in the Amazon,” Int. J. Remote Sensing, vol. 21, no. 1, pp. 173-179, 2000.
[26]M. A. Theseira, G. Thomas, J. C. Taylor, F. Gemmell, and J. Varjo, “Sensitivity of mixture modelling to end-member selection,” Int. J. Remote Sensing, vol. 24, no. 7, pp. 1559-1575, April 2003.
[27]A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, pp. 1483-1492, 1997.
[28]H. Liao, and N. D, “Load profile estimation in electric transmission networks using independent component analysis,” IEEE Trans. Power Systems, vol.18, no. 2, pp. 707-715, May 2003.
[29]J. J. Rieta, F. Castells, C. Sanchez, V. Zarzoso, and J. Millet, “Atrial activity extraction for atrial fibrillation analysis using blind source separation,” IEEE Trans. Biomedical Engineering, vol. 51, pp. 1176-1186, July 2004.
[30]T. W. Lee, M.S. Lewicki, and T. J. Sejnowski, “ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1078-1089, October 2000.
[31]C. J. Liu, “Enhanced independent component analysis and its application to content based face image retrieval,” IEEE Trans. Systems, Man and Cybernetics, vol. 34, no. 2, pp. 1117-1127, April 2004.
[32]D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” Advances in neural information processing systems, pp. 556-562, 2001.
[33] P. Sajda, S. Du, T. R. Brown, R. Stoyanova, D. C. Shungu, X.-L. Mao and L. C. Parra, “Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain,” IEEE Trans. Medical Imaging, vol. 23, no. 12, pp. 1453-1465, December 2004.
[34]Q. Du and C.-C. Chein, “Linear mixture analysis-based compression for hyperspectral image analysis,” IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 4, pp. 875-891, April 2004.
[35]J. M. P. Nascimento and J. M. B. Dias, “Vertex component analysis: a fast algorithm to unmix hyperspectral data,” IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 4, pp. 898-910, April 2005.
[36]J. M. P. Nascimento and J. M. B. Dias, “Does independent component analysis play a role in unmixing hyperspectral data?” IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 1, pp. 175-187, Jan. 2005.
[37]A. Singh, “Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sens., vol. 10, no. 6, pp. 989-1003, 1989.
[38]A.A. Nielsen, K. Conradsen, and J. J. Simpson, “Multivariate alteration detection (MAD) and MAF post-processing in multispectral, bi-temporal image data: New approaches to change detection studies,” Remote Sensing of Environment, vol. 64, pp. 1-19, 1998.
[39]X. Dai and S. Khorram, “The effects of image misregistration on the accuracy of remotely sensed change detection,” IEEE Trans. Geosci. Remote Sensing, vol. 36, no. 5, pp. 1566-1577, September 1998.
[40] Y. Z. Lin and P. F. Hsieh, “Change identification of remote sensing images based on textural and spectral features,” IGARSS’05. Proceedings, vol. 3, pp. 25-29, July 2005.
[41] Q. Jackson and D. A. Landgrebe, “Adaptive Bayesian contextual classification based on Markov random fields,” IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 2454-2463, November 2002.
[42] Ifarraguerri. A. and C-I. Chang, “Multispectral and hyperspectral image analysis with convex cones,” IEEE Trans. Geosci. Remote Sensing, vol. 37, no. 2, pp. 756-770, March 1999.
[43] T.-C. Wu and H.-H Chen, “Study on the regional characteristics of landslide in Taiwan,” Master of Forestry and Resource Conservation, National Taiwan University, Taiwan, 1993.