光學式衛星影像雲層處理之研究

The Study on Cloud Processing in Optical Satellite Imagery

徐逸祥(Yi-Shiang Shiu) ； 朱子豪(Tzu-How Chu)

《航測及遙測學刊》 17卷2期 (2013/08) Pp. 115-134

https://doi.org/10.6574/JPRS.2013.17(2).3

摘要

利用光學式衛星影像進行土地利用判釋或農作物產量估測時，雲層覆蓋是無法避免的干擾之一。就具有厚雲層的影像而言，本研究以單時期影像及區域增長（region growing）之方式偵測並切除無法還原地物資訊的厚雲層及其雲影。具有薄雲的影像則以傅利葉（Fourier）分析建立薄雲的數學模式，再以此模型薄雲並還原薄雲底下的地物光譜資訊，雖然在模式建立階段需兩時期影像，但建立後模式對其它影像進行去雲處理時則僅需單時期資訊。研究成果顯示，厚雲及雲影偵測之整體精度皆可達到90％以上。薄雲去除方面，薄雲過濾器提升了約4％的分類精度，亦減輕薄雲對正規化差異植被指數（normalized difference vegetation index, NDVI）的影響，改善程度在統計上皆達到顯著性（p＜0.01）。本研究成果可應用在土地利用判釋和農作物產量估測中的影像前處理程序，除減少去除雲層的人力，亦可增加衛星影像的利用度。

關鍵字

去雲 ； 光學式衛星影像 ； 地物判釋 ； 區域增長 ； 傅利葉分析

並列摘要

Cloud cover is an inevitable interference when mapping land use/cover with optical satellite imagery. In this study, we apply region growing processing to delineate unrecoverable thick cloud and use Fourier analysis to recover ground information from hazy areas with single temporal imagery. Several methodologies across literature successfully solve cloud problems, but most methods require additional cloud-free reference areas or imagery, which may be unavailable in the real world. Moreover, visual methods rather than quantitative methods are used for assessing results, which can be subjective and arbitrary. Most importantly, the feasibility of applying haze-off imagery to image classification is seldom discussed.To overcome the existing limits, expert method is applied to assess the thick cloud delineation and image classification and normalized difference vegetation index (NDVI) is used to evaluate the recovery degree of ground information after the haze-off processing for quantitative verification of the results. This study revises the image enhancement and region growing algorithm to delineate unrecoverable thick cloud. Accuracy assessment shows the overall accuracy of delineation could be 90% above in each study area. For hazy areas, Fourier analysis is used to reduce haze interference and recover ground information. The proposed haze filter increases the overall accuracy of the whole scenes by about 4%. The overall accuracy of hazy areas in the imagery increases the most (by 6%), while that of shadow areas decreased slightly. The influence of haze on NDVI is also reduced with statistical significance (p ＜ 0.01). Both thick cloud and hazy areas processing can be achieved with no cloud-free area or reference imagery required. Future applications include preprocessing of satellite imagery in land use/cover mapping, which can decrease the manpower to interpret and remove cloud areas and increase the usability of the satellite imagery.

並列關鍵字

Cloud removal ； land features interpretation ； region growing ； Fourier analysis