|Abstract: ||近年來，雖然遙測影像空間與頻譜解析度日益改善，但隨之衍生而來的問題卻是更多未知的訊號源與未定義的目標物被遙測感測器所一併擷取。因此，有效提升目標物偵測的正確性是本研究的主要目標。本論文分別針對合成孔徑雷達(Synthetic aperture radar, SAR)影像與高光譜(Hyperspectral image)影像提出一有效的目標物偵測技術，我們所提出的方法可改善目標物偵測的正確性，可進一步提升後續遙測影像分析運用。 在SAR影像中，本研究提出一結合影像區間與資料模型為基礎之油污偵測法，可減少因斑駁性雜訊造成像素為基礎之目標物偵測的困難。以衡量保持法(Moment-preserving method)將影像進行切割分類，再利用影像切割結果決定油污區域，並分別建立油污與海洋資料模型。接著，依所建立之資料模型，結合廣義概度比值測試(Generalized likelihood ratio test, GLRT)準則，提出一以影像區間為基礎之油污偵測法，在固定錯誤警報機率(Constant false alarm rate, CFAR)的條件下，可自動決定決策準則之臨界值判別油污與海洋。最後，由實測的SAR影像結果，不管對於訓練或未訓練的影像均有極佳的偵測效能，可驗證以影像區間為基礎之油污偵測法可達海洋油污自動判別之目的。 在高光譜影像中，由於大量頻帶數造成資料模型建立的困難性，同時，由於受到大氣干擾或是其他雜訊影響造成目標物頻譜的不確定性，使得目標物偵測的效能受到影響。為了克服上述的問題，本研究提出以線性限制最小變異空間濾波器(Linearly constrained minimum variance, LCMV)為基礎結合訊號子空間投影的目標物偵測法。首先，取代單一限制目標物偵測方法，結合訊號子空間投影法設計一個最佳多限制濾波器。接著，藉由將濾波器權重投影至訊號子空間，此濾波器可以減少目標物頻譜的估測誤差及減緩因目標物頻譜不確定性所造成的偵測效能衰減。此外，此法具有同時能夠偵測目標物、抑制非目標物與最小化干擾所造成的影響。由實驗中，我們提出三種多限制目標物選擇策略：K-means、主要特徵向量與端元擷取技術。最後，模擬與實驗結果顯示，與一些現有的方法比較，採用K-means策略之訊號子空間法具有最好的偵測效果。此外，也驗證我們所提的方法比較不受目標物頻譜的不確定性的影響。|
In recent years, although the advances in sensor technology make remote sensing images have significantly improved spatial and spectral resolutions, many unknown and undefined targets, referred to as interferences, are also unexpectedly acquired by remote sensing sensors. Therefore, the main objective of the research is to improve detection capability of desired targets effectively. In this dissertation, we propose target detection methods for synthetic aperture radar (SAR) images and hyperspectral images, respectively. The proposed methods can improve target detection accuracy which can improve quality of further analysis results in remote sensing images. In SAR images, we propose a region-based oil spill detection method with data modeling incorporated, which can improve the speckle noise problem encountered in pixel-based detection methods. We apply moment-preserving method to partition SAR images into some proper regions. Then, according to these segmentation results which contain oil spills and sea area, we build the data models for oil spills and sea area, respectively. Next, based on the built data models, we propose a region-based oil detection method by the use of the generalized likelihood ratio test (GLRT) decision rule. Under the condition of constant false alarm rate (CFAR), we may determine a threshold automatically to discriminate oil spills and sea regions. Finally, the experimental results from SAR images validate that the proposed method exhibits excellent detection performance both for trained and untrained images to automatically detect oil spills. Because hyperspectral images have many dimensionalities, it is difficult to build a specific target model. Furthermore, target signature is uncertain due to atmosphere interference or other random noise and target detection performance is seriously affected by the uncertainty of target signatures. To overcome the aforementioned problems, we propose a target detection method, which incorporates signal subspace projection (SSP) with the spatial filter based on linearly constrained minimum variance (LCMV) principle. Instead of using a single constraint on target detection, we first design an optimal filter with multiple constraints by using SSP. Then, by projecting the weights of the detection filter on the signal subspace, the proposed SSP can reduce estimation errors in target signatures and alleviate the performance degradation caused by the uncertainty of target signatures. Furthermore, the SSP approach can simultaneously detect desired targets, suppress undesired targets and minimize the interference effects. In the experiments, we provide three schemes in selecting multiple constraints of the desired target: K-means, principal eigenvectors and endmember extracting techniques. Simulation and experimental results show that the proposed SSP with K-means schemes has the best detection performance, as compared to some existing methods. Furthermore, the proposed SSP with multiple constraints is less sensitive to the uncertainty of target signatures.