利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用

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 dete...

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Main Authors: 謝嘉進, Hsieh, Chia-Chin
Other Authors: 資訊工程學系碩博士班, 謝璧妃, Hsieh, Pi-Fuei
Format: Thesis
Language:English
Published: 2006
Subjects:
spectral unmixing
change detection
光譜解混技術
變遷偵測
Arctic
Online Access:http://ir.lib.ncku.edu.tw/handle/987654321/20952
http://ir.lib.ncku.edu.tw/bitstream/987654321/20952/1/
id ftchengkunguniv:oai:ir.lib.ncku.edu.tw:987654321/20952
record_format openpolar
institution Open Polar
collection National Cheng Kung University: NCKU Institutional Repository
op_collection_id ftchengkunguniv
language English
topic spectral unmixing
change detection
光譜解混技術
變遷偵測
spellingShingle spectral unmixing
change detection
光譜解混技術
變遷偵測
謝嘉進
Hsieh, Chia-Chin
利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
topic_facet spectral unmixing
change detection
光譜解混技術
變遷偵測
description 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
author2 資訊工程學系碩博士班
謝璧妃
Hsieh, Pi-Fuei
format Thesis
author 謝嘉進
Hsieh, Chia-Chin
author_facet 謝嘉進
Hsieh, Chia-Chin
author_sort 謝嘉進
title 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
title_short 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
title_full 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
title_fullStr 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
title_full_unstemmed 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
title_sort 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用
publishDate 2006
url http://ir.lib.ncku.edu.tw/handle/987654321/20952
http://ir.lib.ncku.edu.tw/bitstream/987654321/20952/1/
genre Arctic
genre_facet Arctic
op_relation [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
_version_ 1766302630917701632
spelling ftchengkunguniv:oai:ir.lib.ncku.edu.tw:987654321/20952 2023-05-15T14:28:28+02:00 利用光譜解混技術於亞像元變遷偵測與識別：崩塌地變遷應用 Subpixel Change Detection and Identification Based on Spectral Unmixing: An Application to Change Detection of Landslide 謝嘉進 Hsieh, Chia-Chin 資訊工程學系碩博士班 謝璧妃 Hsieh, Pi-Fuei 2006-07-19 145 bytes application/octet-stream http://ir.lib.ncku.edu.tw/handle/987654321/20952 http://ir.lib.ncku.edu.tw/bitstream/987654321/20952/1/ Eng en_US eng [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 spectral unmixing change detection 光譜解混技術 變遷偵測 thesis 2006 ftchengkunguniv 2016-05-22T05:46:52Z 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 Thesis Arctic National Cheng Kung University: NCKU Institutional Repository

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