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姓名	潘易翰(Ilham Adi Panuntun)  查詢紙本館藏	畢業系所	遙測科技碩士學位學程
論文名稱	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 (Proposed LSST-Former Deep Learning Architecture based on Few-Shot Learning for Mangrove Loss Detection with a Small Dataset)
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摘要(中)	紅樹林是提供各種生態和社會經濟效益的關鍵生態系統，但它們受到森林砍伐和城市化等人類活動的威脅。傳統的紅樹林損失監測方法依賴於勞動密集和耗時的現場調查或高解析度衛星圖像分析，通常在空間覆蓋和時間分辨率上存在限制。 LSST-Former架構整合了FCN、基於Transformer的模型和少樣本學習技術的優勢，以應對使用小數據集進行紅樹林損失檢測的挑戰。 Transformer已經在捕捉長程依賴性和從序列數據中學習方面取得了顯著成功，而少樣本學習使模型能夠很好地對未見過的類別或任務進行泛化，並且具有有限的訓練示例。通過結合這些方法，LSST-Former旨在有效地從異構類別中學習。我們的實驗結果展示了LSST-Former相對於現有的深度學習架構（如隨機森林、支援向量機、U-Net、LinkNet、Vision Transformer、SpectralFormer、MDPrePost-Net 和SST-Former）的優越性能，凸顯了其在紅樹林保護和管理工作中實際應用的潛力。
摘要(英)	Mangroves are crucial ecosystems that provide various ecological and socio-economic benefits, but they are under threat from anthropogenic activities such as deforestation and urbanization. Traditional methods for monitoring mangrove loss rely on labor-intensive and time-consuming field surveys or high-resolution satellite imagery analysis, which are often limited in spatial coverage and temporal resolution. The LSST-Former architecture integrates the strengths of both FCN, Transformer-based models, and few-shot learning techniques to address the challenges of mangrove loss detection with small datasets. Transformers have demonstrated remarkable success in capturing long-range dependencies and learning from sequential data, while few-shot learning enables models to generalize well to unseen classes or tasks with limited training examples. By combining these approaches, LSST-Former aims to learn from heterogeneous classes effectively. Our experimental results showcase the superior performance of LSST-Former compared to existing deep learning architectures such as random forest, Support Vector Machine, U-Net, LinkNet, Vision Transformer, SpectralFormer, MDPrePost-Net, and SST-Former, highlighting its potential for practical applications in mangrove conservation and management efforts.
關鍵字(中)	★ 紅樹林喪失檢測 ★ 少樣本學習 ★ Transformer ★ 全卷積網絡 (FCN)	關鍵字(英)	★ mangrove loss detection ★ few-shot learning ★ Transformer ★ Fully Convolutional Network (FCN)
論文目次	摘要 i Abstract ii Acknowledgement 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 Loss 5 2.3. Remote Sensing for Mangrove 5 2.4. Deep Learning for Remote Sensing 6 2.5. Related Works 7 CHAPTER III STUDY AREA AND METHODS 8 3.1. Study Area 8 3.2. Datasets 9 3.2.1. Sentinel-2 Pre-Processing 9 3.2.2. Input Data for Model 12 3.3. Methods 14 3.4. Proposed Deep Learning Architecture 15 3.4.1. FCN Architecture 15 3.4.2. Transformer Architecture 17 3.5. Evaluation Assessment 21 3.6. Validation of Universal Applicability Model 22 3.7. Implementation Detail 23 CHAPTER IV RESULTS AND DISCUSSION 24 4.1. Proposed Deep Learning Architecture Result 24 4.2. Ablation Experiments 25 4.2.1. The influence of different training sizes 25 4.2.2. Effects of Each Extractor Part 26 4.2.3. The Impact of Mangrove and Vegetation Indices 26 4.2.4. Effects of Parameters 27 4.3. Examine the Contrast with Other Well-Established Architectures 28 4.4. Universal Applicability of the Model 30 4.5. Discussion 33 CHAPTER V CONCLUSIONS AND FUTURE WORK 40 5.1. Conclusions 40 5.2. Future Work 40 References 41
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指導教授	陳映濃(Chen, Ying-Nong)	審核日期	2024-6-24
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