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華藝線上圖書館全期下載(Full Issue Download) : 27(3).pdf
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Pages : 131-150    結合作物量測指標與無人機多光譜影像於茶樹生長評估
論文名稱 結合作物量測指標與無人機多光譜影像於茶樹生長評估
Title Combining Crop Physiological Measurements and Unmanned Aerial Vehicle (UAV) Multispectral Images in Tea Tree Growth Assessment
作者 莊忠翰、陳忠義、蔡慧萍
Author Zhong-Han Zhuang, Chung-I Chen, Hui Ping Tsai
中文摘要 茶樹生長狀況監測,除能瞭解各栽植階段之生理狀況,更可推估植物對逆境的反應及耐受性。本研究結合作物生理量測及無人飛行載具 (Unmanned Aerial Vehicle, UAV) 多光譜影像,發展一快速非破壞性且大面積的茶樹生長評估方法。同時討論自然和慣行農法茶樹生理表現與生長狀態差異,並分析生理數值和光譜植生指標的相關性。初步發現,自然農法茶樹具備韌性,且紅邊波段和作物生理數值相關性較高。本研究之快速且大面積的茶樹生長評估方法,有助於茶園耕種管理,亦可成為目前極端氣候下早期預警之重要參考依據。
Abstract Monitoring tea tree growth status is essential for understanding physiological status at each planting stage and vegetation resilience to environmental stresses. The present study aims to develop a rapid, nondestructive, and large-area tea tree assessment method by combining crop physiological measurements and Unmanned Aerial Vehicle (UAV) multispectral images. Additionally, the differences in physiological performance and growth status of natural-based and conventional-based tea tree practices were discussed. Furthermore, the correlation between physiological measurements and spectral vegetation indexes was analyzed. The preliminary results have shown that the natural-based practice tea trees present high resilience, and the red edge spectral band has a higher correlation with crop physiological measurements. The proposed rapid, nondestructive, and large-area tea tree assessment method demonstrates a great opportunity for tea tree cultivation and management and future applications on early warning in the current extreme climate.
關鍵字 無人飛行載具、植物生理數值、光譜植生指標
Keywords Unmanned Aerial Vehicle, Physiological Measurements, Spectral Vegetation Indexes
Pages : 151-164    可見光及紅外熱影像之不同融合法於混凝土橋梁內部劣損檢測研究
論文名稱 可見光及紅外熱影像之不同融合法於混凝土橋梁內部劣損檢測研究
Title A Study on Different Fusion Methods of Visible Light and Infrared Thermal Images for Internal Damage Detection of Concrete Bridges
作者 張怡茹、高書屏、王豐良、林志憲
Author Yi-Ru Zhang, Szu-Pyng Kao, Feng-Liang Wang, Jhih-Sian Lin
中文摘要 橋梁檢測可使用紅外熱影像顯示其內部劣損,但難辨別劣損位置,將其與可見光影像融合,即可獲得影像細節及溫度異常處。本研究使用小波融合 (WT)、拉普拉斯金字塔轉換 (LP)、非下採樣輪廓波轉換 (NSCT)、非下採樣剪切波轉換 (NSST),對混凝土橋梁內部劣損影像做融合,量測及比較內部劣損面積精度。實驗顯示LP難呈現紅外熱影像訊息,WT、NSCT及NSST能有效融合兩種影像,NSCT在污漬表面能凸顯紅外熱影像。NSCT與其他非破壞性檢測方法得到之面積相差0.057 m^2、0.005 m^2,NSST為0.094 m^2、0.007 m^2,WT為0.153 m^2、0.007 m^2。NSCT與實際面積最相近,且不受橋面污漬影響,故本研究推薦NSCT作為混凝土橋梁檢測使用。
Abstract Bridge inspection can use infrared thermal images to display internal damage, but it is difficult to identify the location of damage. By combining infrared thermal images with visible light images, image details and temperature anomalies can be obtained. This study uses Wavelet Transform (WT), Laplacian Pyramid (LP), Nonsubsampled Contourlet Transform (NSCT) and Nonsubsampled Shearlet Transform (NSST) to fuse reinforced concrete bridge images, measure, and compare the accuracy of internal damaged areas. This study shows that LP is difficult to highlight the information of the original infrared thermal image, WT, NSCT and NSST can effectively integrate visible light image and infrared thermal image, and NSCT can highlight infrared thermal image. The area difference between NSCT and other non-destructive detection methods is 0.057 m^2, 0.005 m^2, NSST is 0.094 m^2, 0.007 m^2, WT is 0.153 m^2, 0.007 m^2. NSCT is the closest to the actual area and is not affected by the stains on the bridge deck, so this study recommends NSCT to be used for the detection of concrete bridges.
關鍵字 非破壞性檢測、影像融合、內部劣損檢測
Keywords Nondestructive Testing, Images Fusion, Internal Damage Detection
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