臺灣博碩士論文加值系統

本研究針對遙測影像進行地理校正後的應用，對於環境監控、農作物判釋，搭配機載高光譜儀獲取影像。利用自適應相關估計法對高光譜影像進行物種判釋及分類，操作影像處理軟體ENVI將經過地理校正的影像做分類及判釋。
利用飛行載具的飛行姿態紀錄，將因飛行姿態變動而扭曲之影像進行校正，根據飛行載具的高度、經緯度和方向記錄成輸入參數，針對遙測影像扭曲的部分進行校正，並且設定影像的北方朝向上方，再與較精確的Google地圖比較是否有誤差。
高光譜影像每一像素包含了反射光譜，每一波長都有不同的反射值，光譜曲線是所有波長的反射值形成的連續曲線。本研究針對可見光及短波常紅外線的波長區域之光譜曲線，利用自適應相關估計法將測得的光譜曲線與作為參考標準的光譜曲線進行比較，可以得知判釋的物種可能是何種農作物或礦物質，此外亦可將結果將高光譜影像內的各種相關物種進行分類，如此可得知其分布範圍。
本研究針對遙測高光譜影像的分析，包含飛行資料擷取、地理校正、物種判釋、物種分類等步驟及應用，建立一套遙測高光譜影像的分析流程。

Applications of the geometric-corrected remote-sensing images are used to supervise environment and identify crop. We take advantage of the spectrometer mounted on the aircraft to acquire hyperspectral imaging (HSI). Then use the algorithm of adaptive coherence estimator (ACE) to classify and identify the unknown materials. By performing the software, ENVI, to classify and identify materials in the geometric-corrected images.
Based on the record of aircraft’s attitude, we corrected the distortion of the images. According to the flight height, latitude, longitude and orientation, we can record them as input geometry to correct the distortions. After correcting to north-up images, we compare them to Google map and measure the distance.
Every pixel of a hyperspectral image has spectral reflectance, and there is a different value in different wavelength. The spectral curve is a continue curve that consists the reflectance of all wavelength. In the thesis, we use ACE to compare the spectral curve of VNIR and SWIR to referenced spectral library. Therefore, we can identify what is agriculture or mineral. We also can classify the relative materials and know the range of them.
This study establishes applications and a procedure of hyperspectral imaging analyzing integration system which consists of acquiring the flight data, geometric correction, materials identification and classification.

Contents
誌謝 i
摘要 iii
Abstract iv
Contents v
List of Figures viii
List of Tables xii
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Organization 2
Chapter 2 Transactions on Remote Sensing and Geoscience 3
2.1 Imaging Geometric Correction 3
2.1.1 Introduction 3
2.1.2 Methods of Geometric Correction on Remote Sensing Image 5
Chapter 3 Hyperspectral Imaging Analysis 9
3.1 Hyperspectral Image 9
3.1.1 Introduction 9
3.1.2 Aerial hyperspectral imagery 12
3.2 Materials identification 13
3.2.1 Introduction 13
3.2.2 SCSMT 13
3.2.3 ENVI 20
3.3 Supervised Classification 26
3.3.1 Introduction 26
3.3.2 Training Data 26
Chapter 4 Method of Hyperspectral Imaging Integration System 27
4.1 Geometric Correction 27
4.1.1 System Errors 27
4.1.2 Input Geometry 28
4.2 Hyperspectral Material Identification 32
4.2.1 Spectral Library 32
4.2.2 Matching Spectral Shape 32
4.2.3 Supervised Classification 35
Chapter 5 Data Acquisition and Analyzation 36
5.1 Aerial Photograph 36
5.1.1 Location 1 36
5.1.2 Location 2 40
5.1.3 Location 3 44
5.2 Material Identification 49
5.2.1 Location 1 49
5.2.2 Location 2 57
5.2.3 Location 3 65
5.3 Supervised Classification 72
5.3.1 Location 1 72
5.3.2 Location 2 74
5.3.3 Location 3 76
Chapter 6 Conclusions 79
References 81

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