使用Sentinel -2 影像提出空間、光譜與時間的深度學習架構製作佛羅里達州西南部於2017年受艾瑪颶風影響之紅樹林退化圖

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：83

、訪客IP：3.135.207.220

姓名

杰努庭(Ilham Jamaluddin) 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Sentinel -2 影像提出空間、光譜與時間的深度學習架構製作佛羅里達州西南部於2017年受艾瑪颶風影響之紅樹林退化圖
(Proposed Spatial-Spectral-Temporal Deep Learning Architecture for Mangrove Degradation Mapping Affected by Hurricane Irma 2017 Using Sentinel-2 Data in Southwest Florida)

相關論文

★ 應用多核特徵線嵌入法進行高光譜影像分類	★ 基於SIFT演算法進行車牌認證
★ 利用自適性權重估測機制改善傳統爬山演算法之對焦問題	★ 以核心模糊最近特徵線轉換法做人臉辨識
★ 利用模糊最近特徵線轉換做人臉辨識	★ 基於Leap Motion之三維手寫中文文字特徵擷取
★ 使用人臉辨識強化VPN身份認證	★ 應用核心最近特徵線轉換做人臉辨識
★ 應用相鄰最近特徵空間轉換法於跌倒偵測	★ 利用深度學習方法檢測震前電離層異常
★ 建議的 LSST-Former 深度學習架構基於少樣本學習，用於小資料集的紅樹林損耗檢測	★ 衛星降水資料於高衝擊天氣和滑坡事件的應用研究
★ 以深度學習進行遙測影像植生區域偵測	★ 基於VIT及向日葵8號氣象衛星台灣區域雨量預測之可行性評估
★ 基於SPOT-7衛星影像之台灣土地使用分析	★ 基於衛星影像之台灣土地利用分析

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]

本電子論文使用權限為同意立即開放。
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。
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)

紅樹林是生長在熱帶和亞熱帶氣候區周圍潮間帶的獨特植被，對人類及周邊生態系統有諸多益處（如海岸防護植被、儲碳植被等），紅樹林地圖是了解紅樹林狀況和狀況的重要訊息，衛星影像是廣泛用於紅樹林測繪的數據之一。自然災害事件可能導致紅樹林退化包括颶風事件。在2017 年颶風艾瑪襲擊了佛羅里達州西南部沿海地區導致紅樹林退化。本研究的目的是受颶風艾瑪影響的紅樹林退化地圖提出空間-光譜-時間的深度學習 (DL) 架構。在這項研究中，原本研究區域內的紅樹林區域在颶風事件之後已經退化，透過艾瑪颶風前後的衛星圖像之間的關係可以更深入地了解退化的紅樹林地區。本研究使用免費提供的 Sentinel-2 圖像。所提出的 DL 架構由兩個子模型組成：第一個子模型是 pre-post 深度特徵提取器，利用卷積長短期記憶提取颶風事件前後衛星圖像的時空關係（ConvLSTM）和第二個子模型是典型的全卷積網絡（FCN）分類，將從第一個子模型中提取的特徵分為三類。根據實驗結果，該模型的算法輸出的完整和退化紅樹林類的交叉聯合（IoU）得分分別為96.47％和96.82％，而地圖用戶對生成的地圖的完整和退化紅樹林類的準確度分別為分別為 96.80% 和 95.40%。所提出的模型比其他現有的 FCN 模型（U-Net、LinkNet、FPN 和 FC-DenseNet）取得了更好的結果

摘要(英)

Mangroves are unique vegetation that grows in the intertidal zone around tropical and subtropical climate areas and has many benefits for humans and the surrounding ecosystem (e.g. coastal protection vegetation, carbon storage vegetation, etc.). Mangrove map is important information to know the condition and status of mangrove forests. Satellite imagery is one of the data that is widely used for mangrove mapping. Some natural hazard events can cause mangrove degradation including hurricane events. In 2017, Hurricane Irma hit the southwest Florida coastal zone and caused mangrove degradation. The goal of this study is to propose spatial-spectral-temporal deep learning (DL) architecture for mangrove degradation mapping that was affected by Hurricane Irma. In this study, the degraded mangroves are the mangrove area before the hurricane but were degraded after the hurricane event. The relationship between satellite imagery before and after Hurricane Irma can provide a deeper understanding of degraded mangrove areas. This study used freely available Sentinel-2 imagery. The proposed DL architecture consists of two sub-models: the first sub-model is the pre-post deep feature extractor to extract the spatial-spectral-temporal relationship of satellite imagery before and after the hurricane event by using convolutional long-short term memory (ConvLSTM) and the second sub-model is typical fully convolutional network (FCN) classification to classify the extracted features from the first sub-model into three classes. Based on experiment results, the algorithm output intersection over union (IoU) score of intact and degraded mangrove classes from the proposed model are 96.47% and 96.82%, respectively, while the map user’s accuracy of intact and degraded mangrove class from the produced map are 96.80% and 95.40%, respectively. The proposed model achieved better results than other existing FCN models (U-Net, LinkNet, FPN, and FC-DenseNet).

關鍵字(中)

★ Sentinel-2
★ 紅樹林
★ 退化
★ 颶風
★ 卷積
★ LSTM
★ 深度學習

關鍵字(英)

★ Sentinel-2
★ mangroves
★ degradation
★ hurricane
★ convolutional
★ LSTM
★ deep learning

論文目次

摘要 i
Abstract ii
Acknowledgment iii
Table of Contents iv
List of Figures vi
List of Tables vii
CHAPTER I INTRODUCTION 1
1.1. Background 1
1.2. Challenge and Objectives 3
CHAPTER II LITERATURE REVIEW 4
2.1 Mangroves 4
2.2. Mangrove Degradation 5
2.3. Remote Sensing for Mangrove 5
2.4. Deep Learning for Remote Sensing 7
2.5. Related Works 8
CHAPTER III Study Area and Methods 9
3.1. Study Area 9
3.2. Dataset 10
3.2.1. Sentinel-2 Pre-Processing 10
3.2.2. Input Data for Model 13
3.2.3. Map Accuracy Assessment Reference Samples 16
3.3. Methods 17
3.4. Proposed Deep Learning Architecture 18
3.4.1. Pre-Post Deep Feature Extractor 19
3.4.2. FCN Classifier 21
3.5. Evaluation Assessments 22
3.5.1. Algorithm Output Evaluation 23
3.5.2. Map Accuracy Assessment 23
3.6. Implementation Details 24
CHAPTER IV Results and Discussion 26
4.1. Proposed Deep Learning Architecture Result 26
4.2. Ablation Experiments 29
4.2.1. Effects of Spectral Indices 29
4.2.2. Effects of Each Extractor Part 31
4.3. Comparison with Existing Architecture 32
4.4. Analysis of Intact and Degraded Mangrove Map 37
4.5. Discussion 38
CHAPTER V Conclusions and Future Work 43
5.1. Conclusion 43
5.2. Future Work 43
References 44

參考文獻

[1] Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and Distribution of Mangrove Forests of The World Using Earth Observation Satellite Data. Glob. Ecol. Biogeogr. 2011, 20, 154–159.
[2] Tang, W.; Zheng, M.; Zhao, X.; Shi, J.; Yang, J.; Trettin, C. Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation. Sustainability 2018, 10, 472
[3] Barua, P.; Rahman, S. Sustainable Livelihood of Vulnerable Communities in Southern Coast of Bangladesh through the Uti-lization of Mangroves. Asian J. Water Environ. Pollut. 2019, 16, 59–67
[4] Shahbudin, S.; Zuhairi, A.; Kamaruzzaman, B.Y. Impact of Coastal Development on Mangrove Cover in Kilim river, Langkawi Island, Malaysia. J. For. Res. 2012, 23, 185–190
[5] Carugati, L.; Gatto, B.; Rastelli, E.; Martire, M.L.; Coral, C.; Greco, S.; Danovaro, R. Impact of Mangrove Forests Degradation on Biodiversity and Ecosystem Functioning. Sci. Rep. 2018, 8, 13298.
[6] Hamilton, S.E.; Casey, D. Creation of a High Spatio-Temporal Resolution Global Database of Continuous Mangrove Forest Cover for the 21st Century (CGMFC-21). Glob. Ecol. Biogeogr. 2016, 25, 729–738
[7] Zhen, J.; Liao, J.; Shen, G. Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data. Sensors 2018, 18, 4012
[8] Spencer, T.; Schuerch, M.; Nicholls, R.J.; Hinkel, J.; Lincke, D.; Vafeidis, A.T.; Reef, R.; McFadden, L.; Brown, S. Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model. Glob. Planet. Chang. 2016, 139, 15–30
[9] Gandhi, S.; Jones, T. Identifying Mangrove Deforestation Hotspots in South Asia, Southeast Asia and Asia-Pacific. Remote Sens. 2019, 11, 728
[10] Rivera-Monroy, V.H.; Danielson, T.M.; Castañeda-Moya, E.; Marx, B.D.; Travieso, R.; Zhao, X.; Farfan, L.M. Long-term demography and stem productivity of Everglades mangrove forests (Florida, USA): Resistance to hurricane disturbance. For. Ecol. Manag. 2019, 440, 79–91.
[11] McCarthy, M.J.; Jessen, B.; Barry, M.J.; Figueroa, M.; McIntosh, J.; Murray, T.; Schmid, J.; Muller-Karger, F.E. Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida. Remote Sens. 2020, 12, 1740.
[12] Diniz, C.; Cortinhas, L.; Nerino, G.; Rodrigues, J.; Sadeck, L.; Adami, M.; Souza-Filho, P.W.M. Brazilian Mangrove Status: Three Decades of Satellite Data Analysis. Remote Sens. 2019, 11, 808.
[13] Mondal, P.; Liu, X.; Fatoyinbo, T.E.; Lagomasino, D. Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa. Remote Sens. 2019, 11, 2928.
[14] Kamal, M.; Phinn, S.; Johansen, K. Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Mul-ti-Resolution Image Datasets. Remote Sens. 2015, 7, 4753–4783.
[15] Wan, L.; Zhang, H.; Lin, G.; Lin, H. A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image. Ann. GIS 2019, 25, 45–55.
[16] Guo, M.; Yu, Z.; Xu, Y.; Huang, Y.; Li, C. ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data. Remote Sens. 2021, 13, 1292.
[17] Guo, Y.; Liao, J.; Shen, G. Mapping Large-Scale Mangroves along the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model and Landsat Data. Remote Sens. 2021, 13, 245.
[18] McCarthy, M.J.; Jessen, B.; Barry, M.J.; Figueroa, M.; McIntosh, J.; Murray, T.; Schmid, J.; Muller-Karger, F.E. Mapping hurricane damage: A comparative analysis of satellite monitoring methods. Int. J. Appl. Earth Obs. Geoinf. 2020, 91, 102134.
[19] Lee, C.K.F.; Duncan, C.; Nicholson, E.; Fatoyinbo, T.E.; Lagomasino, D.; Thomas, N.; Worthington, T.A.; Murray, N.J. Mapping the Extent of Mangrove Ecosystem Degradation by Integrating an Ecological Conceptual Model with Satellite Data. Remote Sens. 2021, 13, 2047.
[20] Shi, X.; Chen, Z.; Wang, H.; Yeung, D-Y. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Comput. Vis. Pattern Recognit. 2015, 2, 802–810.
[21] Lugo, A.E; and Snedaker, S.C. The Ecology of Mangroves. Ann. Rev. Ecol. Syst. 1974, 5: 39‐64.
[22] Biswas, S.R; Mallik, A.U; Choudhury, J.K; Nishat, A. A unified framework for the restoration of Southeast Asian mangroves—bridging ecology, society and economics. Wetlands Ecol Manage. 2009, 17, 365–383.
[23] Sippo, J.Z.; Lovelock, C.E.; Santos, I.R.; Sanders, C.J.; Maher, D.T. Mangrove mortality in a changing climate: An overview. Estuar. Coast. Shelf Sci. 2018, 215, 241–249.
[24] Lillesand, T.M; Kiefer, R.W; Chipman, J.W; Remote Sensing and Image Interpretation. 2008. New York: John Willey & Sons Inc.
[25] Huete, A.R and Glenn, E.P. Remote Sensing of Ecosystem Structure and Function. 2011. Boca Raton: CRC Press, Advance in Environment Remote Sensing.
[26] Hoffer, R. M; Davis, S. M; Landgrebe, D. A; Phillips, T. L; Swain, P. H; Lindenlaub, J. C; Silva, L. F. Remote Sensing: The Quantitative Approach. 1928. New York, McGraw-Hill International Book Co., 1978. 405 p.
[27] Roman, A and Ursu, T. Multispektral Satellite Imagery and Airborne Laser Scanning Techniques for the Detection of Archaeological Vegetation Marks. 2016. ISBN 978-606-543-787-6.
[28] LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature. 2015, 521, 436.
[29] Neupane, B.; Horanont, T.; Aryal, J. Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis. Remote Sens. 2021, 13, 808.
[30] Hoeser, T.; Bachofer, F.; Kuenzer, C. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications. Remote Sens. 2020, 12, 3053.
[31] Chen, Y.-N.; Thaipisutikul, T.; Han, C.-C.; Liu, T.-J.; Fan, K.-C. Feature Line Embedding Based on Support Vector Machine for Hyperspectral Image Classification. Remote Sens. 2021, 13, 130.
[32] Long, J.; Shelhamer, E.; Darrell, T. Fully Convolutional Networks for Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2014, 39, 640–651.
[33] Chaurasia, A.; Culurciello, E. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation. In Pro-ceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA, 10–13 December 2017.
[34] Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, Munich, Germany, 5–9 October 2015; Springer International Publishing: Cham, Switzerland, 2015; pp. 234–241.
[35] Jégou, S.; Drozdzal, M.; Vazquez, D.; Romero, A.; Bengio, Y. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Honolulu, HI, USA 21–26 July 2017.
[36] Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 2014, arXiv:1409.1556.
[37] Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. In Pro-ceedings of the Advances in Neural Information Processing Systems 25, Red Hook, NY, USA, 3–6 December 2012; pp. 1097–1105.
[38] Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely Connected Convolutional Networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 2261–2269.
[39] He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1 July 2016; pp. 770–778.
[40] Gallego, A.-J.; Gil, P.; Pertusa, A.; Fisher, R.B. Semantic Segmentation of SLAR Imagery with Convolutional LSTM Sectional AutoEncoders. Remote Sens. 2019, 11, 1402.
[41] Pfeuffer, A.; Schulz, K.; Dietmayer, K. Semantic Segmentation of Video Sequences with Convolutional LSTMs. Comput. Vis. Pattern Recognit. 2019, 1, 1441–1447.
[42] Nabavi, S.S.; Rochan, M.; Wang, Y. Future Semantic Segmentation with Convolutional LSTM. arXiv 2018, arXiv:1807.07946.
[43] Worthington, T.A.; zu Ermgassen, P.S.E.; Friess, D.A.; Krauss, K.W.; Lovelock, C.E.; Thorley, J.; Tingey, R.; Woodroffe, C.D.; Bunting, P.; Cormier, N.; et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci. Rep. 2020, 10, 14652.
[44] Jamaluddin, I.; Thaipisutikul, T.; Chen, Y.-N.; Chuang, C.-H.; Hu, C.-L. MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data. Remote Sens. 2021, 13, 5042.
[45] Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150.
[46] McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432.
[47] Gupta, K.; Mukhopadhyay, A.; Giri, S.; Chanda, A.; Datta Majumdar, S.; Samanta, S.; Mitra, D.; Samal, R.N.; Pattnaik, A.K.; Hazra, S. An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX. 2018, 5, 1129–1139.
[48] Shi, T.; Liu, J.; Hu, Z.; Liu, H.; Wang, J.; Wu, G. New spectral metrics for mangrove forest identification. Remote. Sens. Lett. 2016, 7, 885–894.
[49] Xu, H. Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033.
[50] Szegedy, C.; Liu, Wei.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going Deeper with Convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Bos-ton, MA, USA, 7–12 June 2015.
[51] Rezatofighi, S.H.; Tsoi, N.; Gwak, J.; Sadeghian, A.; Reid, I.D.; Savarese, S. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019.
[52] Saferbekov, S.; Iglovikov, V.; Buslaev, A.; Shvets, A. Feature Pyramid Network for Multi-Class Land Segmentation. Computer Vision and Pattern Recognition. 2018, 2.
[53] Chicco, D.; Warrens, M.J.; Jurman, G. The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment. IEEE Access. 2021, 9, 78368–78381.

指導教授

陳映濃(Chen, Ying-Nong)

審核日期

2022-6-9

推文