博碩士論文 107022006 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:97 、訪客IP:3.215.186.30
姓名 王家翔(Chia-Hsiang Wang)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 以自相似算法進行衛星影像融合和水線判釋
(Self-similarity algorithm for satellite image fusion and waterline interpretation)
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
★ 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★ 使用動態門檻值選取對衛星影像進行非監督式變遷偵測
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 隨著多重感測器應用在遙感探測、電腦視覺等諸多領域的普及,多重感測器產品的融合影像儼然成為新興的話題。主要原因之一是各種感測器可以在同位置提供不同的時空影像。因此,本研究目標為合成來自不同感測器的全色銳化影像,並研究融合影像在水線檢測中的表現。該工作流程以具有低空間解析度但高時間解析度的Sentinel-2衛星影像為例,通過將該數據與全色態影像提供高空間解析度的SPOT-6 衛星影像進行融合。首先,我們將SPOT-6的全色態影像與Sentinel-2的多光譜 (NIR-B-G) 影像進行融合,使用自相似正規化全色銳化 (SimiRegPS)方法融合桃園地區的衛星影像,此自相似性已在自然影像以及各種成像逆問題中得到廣泛的驗證。然後,計算常態化差異水體指數全色銳化 (NDWIP) 以識別水像素。我們使用桃園市政府水務局提供的UAV正射影像驗證了場景一和場景二的8口埤塘,驗證包括旱季(場景一)和雨季(場景二)等兩種場景設定。 在場景一中,融合影像中水線的平均精度在2.99 m和8.05 m 之間。在場景二中,融合影像中水線的平均精度在2.68 m和7.52 m之間。在場景一中,融合影像中水域的平均準確率為85%,而原始影像為 73%。在場景二中,融合影像中水域的平均準確率為84%,而原始影像為72%。綜上所述,本研究顯示通過將 Sentinel-2 與有限的 SPOT-6影像相結合,通過SimiRegPS方法獲得更準確的水線,可以有效地提取水文參數。
摘要(英) With the popularization of multi-sensor applications in remote sensing, computer vision, and many other fields, the fusion of multi-sensor products has become an emerging topic in the community. One of main reasons is the variety of sensors can provide different spatiotemporal images in the same location. Hence, this study aims to compose a panchromatic-sharpened image from heterogenous sensors, and to investigate the performance of the fused image in waterline detection. The workflow is exemplified by Sentinel-2 that has a lower spatial but high temporal resolution, and to merge the data with SPOT-6 that provide much higher spatial resolution in its panchromatic band. We first fuse the panchromatic images of SPOT-6 with the multispectral (NIR-B-G) images of Sentinel-2, by using the Self-similarity Regularized Pansharpening (SimiRegPS) method to fuse the images covering Taoyuan, Taiwan. The self-similarity employed in our design has been extensively examined in natural images as well as in various imaging inverse problems. Following that, the Normalized Difference Water Index Pansharpened (NDWIP) is calculated to identify water pixels. We validate 8 ponds as compared with in situ data from Taoyuan Water Resources Department. The validation includes two scenarios: dry season (scenario 1) and wet season (scenario 2). In scenario 1, the averaged accuracy of waterline in the fused image is between 2.99 m and 8.05 m. In scenario 2, the averaged accuracy of waterline in the fused image is between 2.68 m and 7.52 m. Also, the averaged accuracy of water area in the fused image is 85% and 84%, in contrast to 73% and 72% of the original image in scenario 1 and 2, respectively. To conclude, this research has shown the possibility to effectively extract hydrologic parameters by combining Sentinel-2 with limited SPOT-6 images to obtain the more accurate waterline through SimiRegPS method.
關鍵字(中) ★ 水線
★ 凸自相似性正規化
★ 影像融合
★ 衛星遙測
關鍵字(英) ★ waterline
★ convex self-similarity regularization
★ panchromatic sharpening
★ remote sensing
論文目次 Chapter 1 Introduction 1
1.1. Background and Motivation 1
1.2. Pansharpening Method 2
1.3. Advantage of SimiRegPS 3
1.4. Architecture 4
Chapter 2 Related Works 5
2.1. Method of Image Fusion 5
2.2. Method of Waterline Extraction 6
Chapter 3 Study Area 8
Chapter 4 Data and Methodology 9
4.1. Pre-processing of SPOT-6 10
4.2. Pre-processing of Sentinel-2 11
4.3. Merging Two Data Products 13
4.4. Procedure of Pansharpening 15
4.5. Spectral Index for Water Detection 19
4.6. Canny Edge detection 20
4.7. Normalized Different Water Index Pansharpened (NDWIP) 21
Chapter 5 Experimental Results 25
5.1. Data Generation 25
5.2. Validation of the Developed Method 32
Chapter 6 Discussion 48
Chapter 7 Conclusions 52
Reference 54
參考文獻 Afonso, M.V., Bioucas Dias, J. M., & Figueiredo, M. A. T. (2011). An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Transactions on Image Processing, 20(3), 681–695. https://doi.org/10.1109/TIP.2010.2076294
Aiazzi, B., Alparone, L., Baronti, S., & Lotti, F. (1997). Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid. IEEE Transactions on Image Processing, 6(6), 831–843.
Ali, M., & Clausi, D. (2001). Using the Canny edge detector for feature extraction and enhancement of remote sensing images. International Geoscience and Remote Sensing Symposium (IGARSS), 5(C), 2298–2300. https://doi.org/10.1109/igarss.2001.977981
Alparone, L., Baronti, S., Aiazzi, B., & Garzelli, A. (2016). Spatial methods for multispectral pansharpening: Multiresolution analysis demystified. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2563–2576.
Byun, Y., Han, Y., & Chae, T. (2015). Image fusion-based change detection for flood extent extraction using bi-temporal very high-resolution satellite images. Remote Sensing, 7(8), 10347–10363.
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Chavez, P. S., & Kwarteng, A. Y. (1989). Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering & Remote Sensing, 55(3), 339–348.
Chen, C. H. (2012). Twenty-Five Years of Pansharpening: A Critical Review and New Developments. In Signal and Image Processing for Remote Sensing. https://doi.org/10.1201/b11656-31
Chi, C. Y., Li, W. C., & Lin, C. H. (2017). Convex Optimization for Signal Processing and Communications. In Convex Optimization for Signal Processing and Communications. CRC press. https://doi.org/10.1201/9781315366920
Dong, L., Yang, Q., Wu, H., Xiao, H., & Xu, M. (2015). High quality multi-spectral and panchromatic image fusion technologies based on curvelet transform. Neurocomputing, 159, 268–274.
Drought and water shortage crisis in Taiwan in 2021. (2021). https://zh.m.wikipedia.org/zh-tw/2021年臺灣旱災缺水危機
Gilvear, D., Tyler, A., & Davids, C. (2004). Detection of estuarine and tidal river hydromorphology using hyper-spectral and LiDAR data: Forth estuary, Scotland. Estuarine, Coastal and Shelf Science, 61(3), 379–392.
Hsu, R., & Chang, K. C. (2015). The use of innovative software Pix4Dmapper to optimize the process of generating spatial data from UAV’s aerial images. Journal of the Chinese Institute of Civil and Hydraulic Engineering, 27(3), 241–246.
Laben, C. ., & Brower, B. . (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. In United States Patent 6.
Lebrun, M., Colom, M., Buades, A., & Morel, J. M. (2012). Secrets of image denoising cuisine. Acta Numerica, 21, 475–576. https://doi.org/10.1017/S0962492912000062
Lin, C. H., & Bioucas Dias, J. M. (2020). An Explicit and Scene-Adapted Definition of Convex Self-Similarity Prior with Application to Unsupervised Sentinel-2 Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing, 58(5), 3352–3365. https://doi.org/10.1109/TGRS.2019.2953808
Lin, C. H., Ma, F., Chi, C. Y., & Hsieh, C. H. (2018). A Convex Optimization-Based Coupled Nonnegative Matrix Factorization Algorithm for Hyperspectral and Multispectral Data Fusion. IEEE Transactions on Geoscience and Remote Sensing, 56(3), 1652–1667. https://doi.org/10.1109/TGRS.2017.2766080
Lin, C. H., Ma, W. K., Li, W. C., Chi, C. Y., & Ambikapathi, A. M. (2015). Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No-Pure-Pixel Case. IEEE Transactions on Geoscience and Remote Sensing, 53(10), 5530–5546. https://doi.org/10.1109/TGRS.2015.2424719
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
McFeeters, S. K. (2013). Using the normalized difference water index (ndwi) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561. https://doi.org/10.3390/rs5073544
Pai, C. C., Liu, Y. C., Hsiao, Y. S., Lien, H. P., & Lin, P. H. (2015). Analysis of accuracy for UAV-derived topography from a GoPro camera. Journal of Chinese Soil and Water Conservation, 46(3), 142–149.
Parage, V., & Faget, N. (2015). New sensors benchmark report on SPOT7. In Scientific and Technical Research Series. https://doi.org/10.2788/17914
Paris, C., Bioucas Dias, J., & Bruzzone, L. (2019). A Novel Sharpening Approach for Superresolving Multiresolution Optical Images. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1545–1546. https://doi.org/10.1109/TGRS.2018.2867284
Patel. (2019). Color image denoising via sparse 3D. 15(2), 9–25.
Ryu, J. H., Won, J. S., & Min, K. D. (2002). Waterline extraction from Landsat TM data in a tidal flat: A case study in Gomso Bay, Korea. Remote Sensing of Environment, 83(3), 442–456.
Sentinel, E. S. A. (2 C.E.). User Handbook. ESA Standard Document, 64.
Stephenson, N. M. (2016). High Resolution Habitat Suitability Modeling for a Narrow-Range Endemic Alpine Hawaiian Species a Thesis Submitted To the Graduate Division of the University of Hawai ‘ I At Hilo in Partial Fulfillment of the Requirements for the Degree of Master of Scien.
the project of remote sensing to monitoring analysis and management of Water Resources Key Areas. (2020). https://scholars.ncu.edu.tw/en/projects/109年度遙測科技應用於水資源重點區域監測分析及管理委託專業服務案-2
Tu, T. M., Huang, P. S., Hung, C. L., & Chang, C. P. (2004). A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1(4), 309–312. https://doi.org/10.1109/LGRS.2004.834804
Venkatakrishnan, S.V., Bouman, C. A., & Wohlberg, B. (2013). Plug-and-Play priors for model based reconstruction. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 945–948. https://doi.org/10.1109/GlobalSIP.2013.6737048
Wang, C. H., Lin, C. H., Dias, J. M. B., Zheng, W. C., & Tseng, K. H. (2019). Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 3129–3132.
Wetland Conservation Act. (2022). Taiwans’s Wetland Remsar Citzen. https://wetland-tw.tcd.gov.tw/en/Aboutwetlands.php
Yen, C. H., Chen, K. T., Lee, S. P., Liu, C. J., Wu, C. Y., & Chan, H. C. (2015). A feasibility study on unmanned aerial vehicle for river stability. Journal of Soil and Water Conservation, 47(3), 1407-1416 (Chinese).
Zhao, Y., Yang, J., & Chan, J. C. W. (2014). Hyperspectral imagery super-resolution by spatial-spectral joint nonlocal similarity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2671–2679. https://doi.org/10.1109/JSTARS.2013.2292824
指導教授 曾國欣(Kuo-Hsin Tseng) 審核日期 2022-9-28
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