博碩士論文 110022005 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:101 、訪客IP:3.215.186.30
姓名 李品萱(PIN-HSUAN LI)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 使用半自動變異偵測方法偵測地表覆蓋變化
(Using A Semi-Automatic Change Detection Algorithm to Detect Land Cover Changes)
相關論文
★ 結合多種遙測衛星數據觀測湄公河水資源變化★ 利用多時期之衛星影像改進孟加拉地區之地表水量化
★ 利用ALOS SAR影像觀測2008當雄地震同震及震後形變量★ 利用衛星影像觀測2004年印度洋地震震後之海岸地形垂直變化
★ 利用綜合遙測資訊建置之高程模型觀測近岸地形時序變遷★ 整合Sentinel-1與TerraSAR-X 永久散射體雷達差干涉法以監測地表變形
★ 利用區域電離層模式校正Sentinel-1差分干涉以偵測臺灣地表變形★ 利用衛星影像間接建立全台海岸地形模型
★ 應用Sentinel-1衛星TOPS合成孔徑雷達及最小基線長分析技術監測越南河內的地層下陷★ Sentinel-1 Radar Interferometry Decomposes Land Subsidence in Taiwan
★ 以自相似算法進行衛星影像融合和水線判釋★ 基於卷積神經網路於光學衛星影像進行跨衛星之雲偵測
★ 利用衛星遙測資訊於稻米產量預測★ 利用ICESat-2及Sentinel-2反演南海近岸水深
★ 利用行動測深系統產製淺水區深度模型★ 以多元衛星影像監測青藏高原湖泊長期水量變化
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-8-1以後開放)
摘要(中) 地表覆蓋的變化在人為活動及自然作用下是動態且無預警的發生,特別是在自然災害及戰爭事件之緊急事件發生時,其需要一套工作流程來獲取變化的資訊,隨著遙測技術越漸成熟,硬體設備的提升及高解析度的影像容易取得,讓遙測技術可以做為偵測地表覆蓋變化之工具。然而,在現有物件式變化檢測方法進行影像分割時,需要透過調整分割方法的參數及視覺化確認分割圖層的正確性,在實際應用層面中較需要花費時間達成,並且在變化檢測方面使用如人工智慧等工具時,變化檢測的過程不是透明化,無法得知變化特徵的重要性。因此,本研究嘗試發展一個半自動變化檢測之方法,以改善以上不足之處,此方法將基於物件式之變化檢測方法繪製出地表變化覆蓋圖,在嘉義的日常地表覆蓋變化檢測中,透過 SPOT-7 影像找出突然變化之區域,及在花蓮的 918 地震事件中應用 Pléiades影像偵測損毀的橋樑及季節性地表覆蓋。與此同時,我們會在這兩個案例中對影像在相對輻射校正及特徵篩選的變化檢測、影像在未相對輻射校正及未特徵篩選的變化檢測,及未相對輻射校正及使用特徵篩選的變化檢測進行精度評估。
變化檢測的結果在嘉義的案例中可以找到農地覆蓋的變化外,也試圖找出不是季節性變化之區域,最高的總體精度為 86%。而在花蓮案例的總體精度最高達到84%,在花蓮的變化圖中,除了繪製出季節性地變化之外,也繪製出因地震而導致損毀的高寮大橋。在三個地表變化偵測的測試中,比較特別的是影像在有相對輻射校正後的變化檢測精度比未校正影像的變化檢測的成效不佳,其原因與相對輻射校正的方法有所關聯。
摘要(英) The changes in land cover occur dynamically and unpredictably due to both human activities and natural processes. Particularly during emergencies such as natural
disasters and war events, there is a need for a change detection (CD) workflow to obtain information on land cover changes. With the advancement of remote sensing technology, improved hardware, and easy accessibility to high-resolution imagery, remote sensing has become a valuable tool for detecting land cover changes. However, existing object-based CD methods face challenges in practical applications, it is
necessary to spend time adjusting the parameters of the segmentation method and visually analyzing the accuracy of the segmentation layer. This process requires more time to achieve in practical application scenarios. For those algorithms utilizing tools such as artificial intelligence (AI) lack transparency, making it difficult to assess the
importance of change features.
Therefore, this study aims to develop a semi-automatic change detection method to address these limitations. This method utilizes object-based CD to generate land surface change maps. In the case of abrupt change areas in Chiayi, sudden change areas are identified using SPOT-7 imagery. In the case of the 918 earthquake event in Hualien, Pléiades imagery is used to detect damaged bridges and seasonal land surface cover changes. In both cases, an accuracy assessment is conducted for CD with relative radiometric calibration (RRC) and feature screening (FS), CD without relative radiometric calibration and feature screening, and CD without relative radiometric calibration but with feature screening.
The detection results in the Chiayi case reveal not only changes in agricultural land cover but also attempts to identify non-seasonal change areas, with a maximum overall
accuracy of 86%. In the Hualien case, the overall accuracy reaches up to 84%, and the change map includes both seasonal variations and the collapsed Gao-Liao Bridge caused by the earthquake. Among the three tests of our results, it is noteworthy that the CD accuracy after atmospheric correction is less effective compared to CD without relative radiometric calibration, which is associated with the method of atmospheric correction.
關鍵字(中) ★ 變化檢測
★ 影像分割
★ 多變異分析
★ SPOT-7
★ Pléiades
關鍵字(英) ★ Change Detection
★ Image segmentation
★ Multivariate Analysis
★ SPOT-7
★ Pléiades
論文目次 Chapter 1 Introduction 1
1.1 Background and Motivations 1
1.2 Research Area and Event 6
1.3 The structure of Research 8
Chapter 2 Related Work 10
2.1 The Land Cover Change 10
2.2 Using Remote Sensing Technology to Monitor Land Cover Changes 12
2.2.1 The Direct Comparison Method 15
2.2.1.1 Image Transformation Method 15
2.2.1.2 Change Vector Analysis (CVA) 16
2.2.1.3 Region–Line Primitive Association Framework (RLPAF) 17
2.2.2 The Post-Classification Comparison Method 18
2.2.3 Combining Multi-Change Detection Method 19
Chapter 3 Satellite Data 21
3.1 Satellite pour l′Observation de la Terre (SPOT) 21
3.2 Pléiades 23
Chapter 4 Workflow and Methodology 25
4.1 Workflow of the Semi-Automatic Change Detection 25
4.2 Methodology 27
4.2.1 Bi-Image Fusion 27
4.2.2 Images Segmentation 29
4.2.3 The Features Extraction and Selection Based on Parcel Level 37
4.2.4 Semi-Automatic Change Detection 42
4.2.5 Validation of Semi-Automatic Change Detection 45
Chapter 5 Experiment Results 47
5.1 Chiayi County Case (嘉義縣) 47
5.2 Hualien Country Case (花蓮縣) 56
Chapter 6 Discussion 63
6.1 Chiayi County Case 63
6.2 Hualien County Case 65
6.3 The Images Segmentation 68
6.4 The Changed Detection Method’s Accuracy 68
Chapter 7 Conclusions 70
7.1 Summary of This Study 70
7.2 Limitations of the Proposed Study and Future Work 71
Reference 72
參考文獻 Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2010). Slic
superpixels (No. REP_WORK).
Asokan, A., & Anitha, J. J. E. S. I. (2019). Change detection techniques for remote
sensing applications: a survey. Earth Science Informatics, 12(2), 143-160.
Arya Krishnan, Chithira Rakshmi. (2015). Comparative Study on Pansharpening Methods for Satellite Images, International Research Journal of Engineering and Technology, 9(2), 2615-2620.
Baker, C., Lawrence, R. L., Montagne, C., & Patten, D. (2007). Change detection of wetland ecosystems using Landsat imagery and change vector analysis. Wetlands, 27(3), 610-619.
Celik, N. (2018, July). Change detection of urban areas in Ankara through Google Earth engine. In 2018 41st International Conference on Telecommunications and Signal Processing (TSP) (pp. 1-5). IEEE.
Ciriza, R., Sola, I., Albizua, L., Álvarez-Mozos, J., & González-Audícana, M. (2017). Automatic detection of uprooted orchards based on orthophoto texture analysis. Remote Sensing, 9(5), 492.
Classification Algorithms and Methods, “Introduction to Categorisation of Objects from their Data, ” Science Education through Earth Observation for High Schools, [Online]. Available: https://seos-project.eu/classification/classification-c01-p05.html. [Accessed 1 June 2023].
CSRSR, “System and Product,”[Online]. Available: https://www1.csrsr.ncu.edu.tw/rsrs/rsrs_product.php. [Accessed 21 January 2023].
Deng, X., Zhao, C., Lin, Y., Zhang, T., Qu, Y., Zhang, F., Wang, Z., Wu, F. 2014a. Downscaling the impacts of large-scale LUCC on surface temperature along with IPCC RCPs: a global perspective. Energies, 7(4), 2720-2739.
Desclée, B., Bogaert, P., & Defourny, P. (2006). Forest change detection by statistical object-based method. Remote sensing of environment, 102(1-2), 1-11.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5), 1-42.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621
Hölbling, D., Friedl, B., & Eisank, C. (2015). An object-based approach for semi-
automated landslide change detection and attribution of changes to landslide
classes in northern Taiwan. Earth Science Informatics, 8(2), 327-335.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Huang, J., Liu, Y., Wang, M., Zheng, Y., Wang, J., & Ming, D. (2019). Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion. Remote Sensing, 11(21), 2484.
ImageFeatures,“Gray Level Co-occurrence Matrix,” ImageFeatures, [Online]. Available: https://juliaimages.org/ImageFeatures.jl/stable/tutorials/glcm/. [Accessed 1 June 2023].
Kelly, M., Blanchard, S. D., Kersten, E., & Koy, K. (2011). Terrestrial remotely sensed imagery in support of public health: New avenues of research using object-based image analysis. Remote Sensing, 3(11), 2321-2345.
Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., ... & Xu, J. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global environmental change, 11(4), 261-269.
Li, Q., Gong, H., Dai, H., Li, C., He, Z., Wang, W., ... & Mu, T. (2021). Unsupervised hyperspectral image change detection via deep learning self-generated credible labels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9012-9024.
Li, Q., Gong, L., & Zhang, J. (2019). A correlation change detection method integrating PCA and multi-texture features of SAR image for building damage detection. European Journal of Remote Sensing, 52(1), 435-447.
Lister, T. W., Lister, A. J., & Alexander, E. (2014). Land use change monitoring in Maryland using a probabilistic sample and rapid photointerpretation. Applied Geography, 51, 1-7.
Liu, J., Liu, M., Tian, H., Zhuang, D., Zhang, Z., Zhang, W., ... & Deng, X. (2005). Spatial and temporal patterns of China′s cropland during 1990–2000: an analysis based on Landsat TM data. Remote sensing of Environment, 98(4), 442-456.
Liu, S., Zheng, Y., Dalponte, M., & Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. European journal of remote sensing, 53(1), 104-112.
Luo, H., Liu, C., Wu, C., & Guo, X. (2018). Urban change detection based on Dempster–Shafer theory for multitemporal very high-resolution imagery. Remote Sensing, 10(7), 980.
Magnussen, S., & Russo, G. (2012). Uncertainty in photo-interpreted forest inventory
variables and effects on estimates of error in Canada’s National Forest Inventory.
The Forestry Chronicle, 88(4), 439-447.
Mardiris, V., & Chatzis, V. (2016). A Configurable Design for Morphological Erosion and Dilation Operations in Image Processing using Quantum-dot Cellular Automata. Journal of Engineering Science & Technology Review, 9(2).
Massarelli C (2018) Fast detection of significantly transformed areas due to illegal waste burial with a procedure applicable to landsat images. Int J Remote Sens, 39:754–769. https://doi.org/10.1080/01431161. 2017.1390272.
Nielsen, A. A., Conradsen, K., & Simpson, J. J. (1998). Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies. Remote Sensing of Environment, 64(1), 1-
190.
Polykretis, C., Grillakis, M. G., & Alexakis, D. D. (2020). Exploring the impact of various spectral indices on land cover change detection using change vector analysis: A case study of Crete Island, Greece. Remote Sensing, 12(2), 319.
Ruiz Hernandez, I. E., & Shi, W. (2018). A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis. International journal of remote sensing, 39(4), 1175-1198.
Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover
classification using remote sensing and GIS. International Journal of Geosciences,
8(04), 611.
Sheykhmousa, M., Kerle, N., Kuffer, M., & Ghaffarian, S. (2019). Post-disaster
recovery assessment with machine learning-derived land cover and land use
information. Remote sensing, 11(10), 1174.
Shukla, P. R., Skeg, J., Buendia, E. C., Masson-Delmotte, V., Pörtner, H. O., Roberts,
D. C., ... & Malley, J. (2019). Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems.
SINGH, A., 1989, Digital change detection techniques using remotely-sensed data,
International Journal of Remote Sensing, 10(6), pp.989-1003.
Song, W., & Deng, X. (2017). Land-use/land-cover change and ecosystem service
provision in China. Science of the Total Environment, 576, 705-719.
Song, X. P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A., Vermote,
E. F., & Townshend, J. R. (2018). Global land change from 1982 to 2016. Nature,
560(7720), 639-643.
TEEB (2012), The Economics of Ecosystems and Biodiversity in Local and Regional
Policy and Management. Edited by Heidi Wittmer and Haripriya Gundimeda.
Earthscan: London and Washington.
Touati, R., & Mignotte, M. (2017). An energy-based model encoding nonlocal pairwise
pixel interactions for multisensor change detection. IEEE Transactions on
Geoscience and Remote Sensing, 56(2), 1046-1058.
Ullah, S., Bhatti, N., & Zia, M. (2021). Adaptive tuning of SLIC parameter K.
Multimedia Tools and Applications, 80(17), 25649-25672.
Verburg, P. H., Van De Steeg, J., Veldkamp, A., & Willemen, L. (2009). From cover
change to land function dynamics: A major challenge to improve land
characterization. Journal of environmental management, 90(3), 1327-1335.
Wang, Z., Liu, Y., Ren, Y., & Ma, H. (2018). Object-Level double constrained method
for land cover change detection. Sensors, 19(1), 79.
Winkler, K., Fuchs, R., Rounsevell, M., & Herold, M. (2021). Global land use changes
are four times greater than previously estimated. Nature communications, 12(1),
1-10.
Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover
classification and change analysis of the Twin Cities (Minnesota) Metropolitan
Area by multitemporal Landsat remote sensing. Remote sensing of Environment,
98(2-3), 317-328.
Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of
land cover using all available Landsat data. Remote sensing of Environment, 144, 152-171.
指導教授 曾國欣(Kuo-Hsin Tseng) 審核日期 2023-7-31
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明