無人飛行載具(Unmanned Aerial Vehicle, UAV)具有高機動性且低成本的特性，若搭載多光譜感測器，能獲取比傳統航照或遙測衛星更高空間、時間及波譜解析度之高解析度多光譜影像。微型化多光譜相機陣列(Miniature Multiple Camera Array, MiniMCA)是一台具有12個鏡頭之窄波段多光譜相機，其可記錄之波譜範圍從藍光到近紅外光(450-950 nm)，其中包含三個紅色邊緣(Red-edge)波段，波長分別為700 nm、720 nm及750 nm，能推導出更多的植生指標以利植生健康指數的判讀。與寬波段之多光譜衛星影像相比，MiniMCA波段寬度僅10 nm，更能準確的推導各式植生指標，因此MiniMCA所拍攝的多波譜資訊，更適合應用於精準農業、植生監測、地物分類和植物逆境偵測等遙測應用。為了與一般寬波段之可見光相機在影像分類上的能力比較，本研究利用定翼型UAV同時搭載MiniMCA與Canon 5D II DSLR數位相機，於同一測區進行影像資料的收集並進行物件導向式(Object-Based Image Analysis, OBIA)的影像分類與分類精度比較。其中因MiniMCA的不同波段間存在影像錯位問題，因成像距離而造成漸暈現象，以及未經改正的波譜反射值，故須進行波段套合、漸暈改正與輻射改正等影像前處理。最後透過空三平差、密集影像匹配及正射鑲嵌程序，利用高解析度之DSLR影像產製1公尺網格的DSM與10公分解析度之RGB三波段彩色正射影像，並透過相同之DSM產製40公分解析度之MiniMCA十二波段之反射率正射影像如此便能利用正射影像的波譜資訊推導各式植生指標，及整合物高模型(Object Height Model, OHM)，並採用物件導向的物件幾何資訊開發階層式分類法則進行地物分類。經由地物分類大致可以分出以下七大類，樹木、草地、水體、建物、裸露地、道路和其它，其中樹木和草地部分可以再細分為茂密和稀疏植被。本研究針對MiniMCA中的十二波段(MiniMCA-12)、四波段(MiniMCA-4)和三波段(MiniMCA-3)進行分類測試，並比較Canon 5D II可見光三波段(Canon-3)以及Canon-3加入MiniMCA 750 nm波長之紅外光波段組成之四波段(Canon-4)在地物分類上之精度差異。Canon-3和MiniMCA-3的分類kappa指標分別為0.66與0.60，加入紅外光資訊的Canon-4和MiniMCA-4的分類kappa指標分別為0.73與0.72，而使用所有波段的MiniMCA-12分類成果為0.78。成果顯示Canon-4和MiniMCA-4的分類成果相當，與Canon-3和MiniMCA-3比較時可發現加入紅外光可提升整體分類成果，同時說明使用更多的波段數能獲得最好的結果，表示波段數量對於地物分類有顯著之影響。

Comparing with satellite and airplane, Unmanned Aerial Vehicle (UAV) is a portable, high mobility, and low cost platform for obtaining higher spatial, spectral, and temporal resolution images. Miniature Multiple Camera Array (MiniMCA) is a light weight and narrow-band multispectral camera. It is composed by 12 different lenses which can acquire spectral information ranging from 450 nm (blue) to 950 nm (near infrared). These 12 narrow bands include three rededge bands which wavelengths are 700 nm, 720 nm, and 750 nm that can derive more vegetation indices for healthy vegetation identification. The bandwidth of MiniMCA is only 10 nm that can derive more vegetation indices than broad band satellite images for water stress detection, vegetation monitoring, and precision agriculture. In this study, MiniMCA and Canon 5D II DSLR camera are mounted together on a fixed-wing UAV to acquire both high spatial resolution multispectral images and visible light images. Besides, the vegetation classification and the comparison of classification accuracy are performed through Object-Based Image Analysis (OBIA). Since the different bands of MiniMCA have band misregistration effect, vigenetting effect and the digital value need to transfer to reflectance value for precise vegetation indices calculation, it needs to do data preprocessing, including band to band registration, vigenetting effect correction and radiometric correction. In the end, performing through aerial triangulation, dense matching and ortho-mosaic to generate 1 m grid DSM, 10 cm RGB visible light ortho-image and 40 cm MiniMCA multi-spectral ortho-image. By OBIA and combine several vegetation indices and Object Height Model (OHM) to evaluate land cover classification results. The study area can be classified into seven classes which include tree, grass, water body, building, bare land, road, and others. In which, the tree and the grass can be classified as dense vegetation and sparse vegetation. In this study, we use twelve bands of MiniMCA (MiniMCA-12), four bands (MiniMCA-4), and three bands (MiniMCA-3) to classify images and compare the difference of the accuracy of Canon 5D II RGB visible light (Canon-3) and Canon-3 which adds 750 nm of MiniMCA (Canon-4). The kappa index of Canon-3 and MiniMCA-3 is 0.66 and 0.60, respectively. The kappa index of Canon-4 and MiniMCA-4 which add infrared light information is 0.73 and 0.72, respectively. The kappa index of MiniMCA-12 which use all bands is 0.78.The results of classification show that Canon-4 and MiniMCA-4 are comparable. Comparing with Canon-3, we can find when adding infrared light can improve the overall classification results, while using more bands to get the best results. It shows that the number of bands has a significant impact on the classification.

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