臺灣博碩士論文加值系統

了解水深或海底地形對於人類從事近岸海域活動相當重要，近年來隨著國際情勢發展，海底地形測繪也成為政府關切的重點。光學衛星影像具有在短時間內拍攝範圍廣闊的優點，應用於水深反演有其優勢。
另ㄧ方面，衛星影像容易受到雲或霧霾等大氣因素干擾、遮蔽，進而影響水體像元的光譜，使其無法反演出合理的水深。故本研究將衛星影像中受雲霧干擾的水體像元視為混合像元，以線性光譜分解(LSU)估計受其中雲的含量(abundance)，再以線性關係式將其雲成分移除，以萃取像元中的水體資訊，用於水深反演。
本研究使用東沙環礁之WorldView衛星影像，配合空載光達實測資料，進行水深反演及驗證。反演模式包含類神經網路法及模型聯立解法，前者使用實測水深資料樣本訓練網路，後者倚靠實測水深資料歸納出的水體固有光學性質參數(IOPs)輔助。
研究成果顯示，類神經網路法及模型聯立解法皆適用於反演水深，以類神經網路法的精度較高，但會產生少數相對於模型聯立解法的極大誤差。在估計移除雲成分後的雲霧區水深時，誤差隨著原始雲含量的增加而有上升的趨勢。靠環礁外圍之礁臺的混合像元，其所在環境水深較淺(10公尺內)，移除雲成分後，反演水深的精度高於靠環礁內部水深較深(約20公尺)之潟湖的混合像元，並且與純水體像元所反演水深的精度相當。

Water depth and the topography under water provide important information for nearshore human activities. With the intensification of international territory concerns, bathymetric mapping is also gaining attention. With its wide coverage, optical satellite imagery provides an efficient tool for estimating shallow water depth as compared to the traditional field surveying.
On the other hand, the existence of cloud and haze contaminates the spectral signatures, which introduces errors to the depth retrieved. In this research, the contaminated pixels are treated as a mixture of water and cloud component. Linear Spectral Unmixing (LSU) procedure is applied for estimating the cloud abundance in mixed pixels. The cloud component is then removed with a linear function. The “purified” water component is then used for depth retrieval.
In this research, water depth is estimated with two methods, namely, artificial neural network (ANN) and physical model. The former demands in-situ bathymetric samples for training, the latter requires site information of inherent optical properties (IOPs).
The experiments reveal that retrieving depth with ANN generates better result than the physical model, but with few extremely large errors. As for mixed pixels, the error of depth estimation becomes higher when cloud abundance increases. The precision of depth retrieval is higher for mixed pixels at reef flat (within 10 meters in depth) than those in the lagoon (about 20 meters in depth), and the precision generally agrees with those retrieved from water pixels without cloud or haze.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 2
1-1 研究動機與目的 2
1-2 文獻回顧 3
1-3 研究方法 4
1-4 論文架構 6
第二章 研究資料說明 7
2-1 研究區域 7
2-2 水深實測資料 8
2-3 衛星影像 8
2-3-1 大氣改正 10
2-3-2 影像重新取樣 12
第三章 雲成分移除 13
3-1 雲霧偵測 14
3-1-1 基於近紅外光波段特性建立遮罩 14
3-1-2 基於優化薄雲霧轉換建立遮罩 16
3-2 雲成分移除 19
3-2-1 線性混合模式 20
3-2-2 材質選取 23
第四章 水深反演模式 28
4-1 類神經網路法 28
4-1-1 倒傳遞類神經網路 30
4-1-2 網路架構 32
4-1-3 訓練樣本選取 33
4-2 模型聯立解法 34
4-2-1 水體光學性質 35
4-2-2 半解析模型 36
4-2-3 模型聯立求解水深 37
4-2-4 水線約制 39
第五章 成果與討論 41
5-1衛星影像雲成分移除成果 41
5-1-1 WorldView-2衛星影像雲霧偵測 41
5-1-1-1 基於近紅外光波段特性建立遮罩 41
5-1-1-2 基於優化薄雲霧轉換建立遮罩 42
5-1-2 WorldView-3衛星影像雲霧偵測 43
5-1-2-1 基於近紅外光波段特性建立遮罩 43
5-1-2-2 基於優化薄雲霧轉換建立遮罩 44
5-1-3 WorldView-2衛星影像雲含量 45
5-1-4 WorldView-3衛星影像雲含量 45
5-1-5 WorldView-2衛星影像雲成分移除 46
5-1-6 WorldView-3衛星影像雲成分移除 47
5-2 類神經網路法水深反演 48
5-2-1 WorldView-2衛星影像 49
5-2-1-1 WorldView-2衛星影像水體區 50
5-2-1-2 WorldView-2衛星影像雲霧區 51
5-2-2 WorldView-3衛星影像 53
5-2-2-1 WorldView-3衛星影像水體區 54
5-2-2-2 WorldView-3衛星影像雲霧區 55
5-3 模型聯立解法水深反演 58
5-3-1 WorldView-2衛星影像 60
5-3-1-1 WorldView-2衛星影像水體區 61
5-3-1-2 WorldView-2衛星影像雲霧區 62
5-3-2 WorldView-3衛星影像 64
5-3-2-1 WorldView-3衛星影像水體區 65
5-3-2-2 WorldView-3衛星影像雲霧區 66
第六章 結論與建議 70
參考文獻 72

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