臺灣的農業使用人力計算種植面積和農田種類。這種方法效率低，成本高，限制了臺灣在智慧農業的發展，因此本文提出結合空中高光譜感測平台與人工智慧解讀對臺灣農田進行遙感分析，以利未來智慧農業的發展。
地表物質反射率通常被用作研究物體表面性質的重要參數。輻射與反射率的關係中，有許多數學模型需要計算，特別是氣溶膠和水蒸氣對光譜的影響，然而計算大氣模型各種詳細參數的準確值是非常困難的一個問題。
如何對高光譜圖像進行分類是另外一個問題，現階段農田的分類由農試所人工繪製，需要去農田現場調查，時效非常低，往往需要一個星期的時間才能繪製完成。然而通過機器學習演算法對高光譜圖像進行分類，只需要3至4個小時就能完成分類。但是不同演算法會有不同的結果，分類效率也不相同。某些演算法比如類神經網絡演算法需要設置參數，這些參數影響分類結果，需要根據經驗手動設置，因此找到一個最優化參數很困難。
本文提出了一種根據大數據來計算表面反射率的演算法並在臺灣首個自主設計的高光譜儀器上進行了測試。通過所提出的輻照度和大氣校正方法以及地面光譜儀的校正實驗，得到的結果顯示，對太陽光譜數據進行校正時，可見光和近紅外線光譜(VNIR, Visible and Near Infrared）的均方根誤差（RMSE, Root Mean Square Error）為0.0859 Watt/(m2nm)，短波紅外光譜（SWIR, Short Wave Infrared）的RMSE為0.0367 Watt/(m2nm)。將淺灰色靶布計算得到的大氣校正係數(ACC, Atmospheric Correction Coefficient）應用於白色靶布反射率的計算，得到的反射率與真實值的RMSE為0.015。這說明了本文所提出機載高光譜遙感的大氣校正方法的可信度是很高的。
本文對農田高光譜圖像進行有監督和無監督分類，監督分類演算法採用支持向量機（SVM, Support Vector Machine）和倒傳類神經網絡(BP, Back Propagation Neural Network)。無監督分類算法使用K均值（K-means）和迭代自組織數據分析技術算法（ISODATA, Iterative Selforganizing Data Analysis Techniques Algorithm）。實驗結果證明了SVM和ISODATA演算法在處理高光譜這種高維大數據時表現更佳。
新的大氣校正的方法，比傳統的大氣校正方法更簡化，不需要計算複雜的大氣模型，即可對遙感的原始數據進行處理。未來這種方法可以應用在無人機遙感領域。
通過各種監督分類演算法的對比，使用kernel方法的比不使用kernel方法的結果要好的多，BP神經網絡沒有使用kernel方法，預計未來將神經網絡結合kernel方法將大大提高效果。

Taiwan’s agriculture still uses manual methods to count planted areas and species of farmland. This method is inefficient and costly, constraining the development of Taiwan’s traditional agriculture to smart agriculture. This article proposes the use of an aerial hyperspectral platform for remote sensing analysis of Taiwan agriculture.
The reflectance of surface materials is often used as an important parameter for studying the properties of surface objects. In the relationship between radiation and reflectance, there are many mathematical models that need to be calculated, especially the effects of aerosols and water vapor on the spectrum, but it is very difficult to calculate the exact values of various detailed parameters of the atmospheric model.
How to classify hyperspectral images? The classification of farmland is manually drawn by the Agricultural Research Institute, and it is necessary to go to the farmland for site investigation. The efficiency will be very low, and it usually takes a week to draw the farmland graphic distribution. The classification of hyperspectral images by machine learning algorithms can be classified in only 3-4 hours. However, different algorithms will have different results and the efficiency of classification will be different. Some algorithms, such as neural network algorithms, need to set parameters that affect the classification results. It needs to be manually set according to experience, therefore it is difficult to find optimal parameters.
The thesis presents an algorithm for calculating surface reflectance based on data and tested it on Taiwan's first self-designed hyperspectral instrument. Calibration experiments were performed by using the irradiance correction, atmospheric correction methods. The obtained results show that the VNIR (Visible and Near Infrared) Root Mean Square Error is 0.0859 Watt/(m2nm), and the RMSE of SWIR (Short Wave Infrared) is is 0.0367 Watt/(m2nm) when calibrate the solar spectrum data. Applying the light gray Atmospheric Correction Coefficient (ACC) to the white target cloth, the RMSE of the reflectance to the true value is 0.015. This indicates that the method is reliable for atmospheric correction of airborne hyperspectral remote sensing.
The new atmospheric correction method is simpler than the traditional atmospheric correction method, and the raw data of remote sensing can be processed without calculating a complex atmospheric model. In the future, this method can be applied to the field of remote sensing of drones.
Through the comparison of various supervised classification algorithms, the kernel method is much better than the kernel method. The BP neural network does not use the kernel method. It is expected that the neural network combined with the kernel method will greatly improve the effect in the future.

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