Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/10214
標題: 利用空間及頻率濾波器降噪以提升高光譜影像分類
Improving hyperspectral image classification through image denoising by spatial and frequency fliters
作者: 王奕翔
Wang, Yi-Hsiang
關鍵字: 降低雜訊;denoise;高光譜影像;分類準確度;MNF;支持向量機;外埔;hyperspectral;Minimum Noise Fraction;Support Vector Machine
出版社: 土木工程學系所
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摘要: 
隨著光譜影像科技的發展與進步,高光譜影像提供更好的資訊與光譜解析度,本研究以機載高光譜儀所拍攝影像資料,對台中外埔試驗地區進行地物分類,外埔試驗農地為國立中興大學環境保育暨防災科技研究中心所建立,用於長期觀測區域。
  高光譜感測儀之光譜解析度介於427.2~945.7nm,共218個波段,對地物進行分析。高光譜影像的超高維度內含大量且連續之訊息,為降低運算與資料處理效率,利用最小噪聲轉換萃取對高光譜影像進行降維度,並搭配支持向量機對影像進行分類,進一步分析其分類準確度。
  本研究主軸為消除高光譜影像上之條紋雜訊,以頻率濾波及空間濾波兩種方式降低雜訊,其一為快速傅立葉轉換加上高通濾波器將影像轉換到頻率端,降低頻率上之線條雜訊並提升影像處理之速度,其二為雲線內插方式修補前面頻率濾波所無法消除之雜訊,以四條三階多項式進行擬合修補降低離散像元及局部條紋雜訊,藉此提高分類準確度。
本研究經由MNF最小噪聲轉換、快速傅立葉轉換、濾波器再反轉換以及雲線內插修補,最後經由SVM支持向量機分類,由分類成果圖發現降除雜訊後可有效地將影像中的噪聲降除,使高光譜影像之分類準確度由49%提升到78.8%。

 Hyperspectral image provides more information and has better spectral resolution. The Waipu testsite is established by The Center of Environmental Restoration and Disaster Reduction at National Chung Hsing University, and the test field has 200 ha of rice paddy for a long term monitoring.
  The ISIS hyperspectral imager made by National Applied Research Laboratories contains 218 bands with a spectral bandwidth of 3-5 nm between 427.2 nm and 945.7 nm. ISIS is a push-broom scanner with 218 bands in each scan. Feature extraction can be done through Minimum Noise Fraction (MNF) regarded as the most effective approaches to solving the inefficiency of computation. This research applied Support Vector Machine (SVM) to do the classification.
  To dissolve the problem of scattered pixels and noises along the flying track on ISIS hyperspectral image, both frequency and spatial fliters were applied to reduce the noises. And spline-based interpolation is also applied in this research to improve the classification accuracy.
This research applies Minimum Noise Fraction (MNF), Fast Fourier Transform(FFT), Spline and Support Vector Machines(SVMs) to classification. According to the result of classification, the noise contains in ISIS hyperspectral image can be removed effectively. The overall accuracy of ISIS hyperspectral image can be promoted from 49% to 78.8%.
URI: http://hdl.handle.net/11455/10214
其他識別: U0005-2108201316371400
Appears in Collections:土木工程學系所

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