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

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杰努庭(Ilham Jamaluddin) 查詢紙本館藏

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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)

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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

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指導教授

陳映濃(Chen, Ying-Nong)

審核日期

2022-6-9

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