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題名 | 小波轉換於SAR影像資料萃取之研究=A Research on Extracting Information in SAR Images Using Wavelet Transformation |
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作者姓名(中文) | 周建忠; 夏榮生; | 書刊名 | 航測及遙測學刊 |
卷期 | 6:2 2001.07[民90.07] |
頁次 | 頁71-86 |
分類號 | 312.1 |
關鍵詞 | 合成孔徑雷達; 雙重小波轉換; 完全小波轉換; 紋理特徵; Synthetic aperture radar; SAR; Dyadic decomposition wavelet; Overcomplete decomposition wavelet; Texture feature; Multi-resolution analysis; |
語文 | 中文(Chinese) |
中文摘要 | 合成孔徑雷達(SAR)能在任何時段及不同天候下作業,並記錄被測物體之豐富紋理訊息,使其成為遙感探測不可或缺的工具之一。目前衛載SAR之影像不若衛載光學影像有許多波段資料,所以需藉由其影像特質,在較少波段資訊下亦能發揮其長處及實用性。本研究以小波轉換之雙重與完全小波轉換將每一波段的SAR影像,轉換成不同解析度下具不同方向之紋理特徵影像,如此一張SAR影像便可以產生數張具不同解析度下紋理特性之影像,進而可以藉由分類技術,自動化萃取影像中之資訊。由於一般使用之雙重小波轉換後之影像為原影像之四分之一大小,造成影像尺寸縮小問題,本研究以再取樣方式還原影像大小後再進行影像資訊萃取作為。完全小波轉換沒有影像尺寸縮小問題,便於紋理特徵之萃取與後期分類自動化處理。在空載SAR影像試驗研究成果顯示以雙重小波轉換加上再取樣處理後產生之具不同紋理之多張影像,不僅可以作為影像資料自動萃取之憑藉,其分類整體精度也達到77.3%之。當使用完全小波轉換時,其分類整體精度更提昇至85.7%,顯示小波轉換應用於SAR影像之紋理特徵萃取與提昇分類自動化效能之成效。 |
英文摘要 | Synthetic Aperture Radar (SAR) that operates independently of time and weather condition is capable of recording rich texture information and is becoming one of important tools of remote sensing techniques. The current space-borne SAR system can prove only single-band single-polarization image, which provides less band image than space-borne optical images. Then extracting information in the SAR image has to be carried out in a specific way corresponding to the character of the SAR image. The methods of Dyadic Decomposition wavelet or Overcomplete Decomposition wavelet have been applied to the SAR image in order to produce more images with different features and resolutions for automating information extraction by using classification techniques. Image size is reduced to a quarter of an original image while Dyadic Decomposition wavelet transformation has been applied. In order to extract information automatically on the basis of the size of the original image, resampling process is used in this research. However, the resampling process does not need while Overcomplete Decomposition wavelet is applied. An air-bone SAR image has been used in this research and shows that the accuracy of image classification is 77.35% when using Dyadic Decomposition wavelet. But the accuracy of image classification can be promoted to 85.7% when Overcomplete Decomposition wavelet is applied. The efficiency of automatically extracting information in a SAR image on the basis of texture features produced using wavelet-based multi-resolution analysis has been shown. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。