光學式衛星影像雲層處理之研究

Abstract

在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。以往研究的瓶頸在於多數去雲流程皆需要另外的無雲參考區域或是多時期影像，然而真實世界中，這些參考資訊可能難以取得；再者，對於去雲結果的優劣，通常是以質化而非量化的方式來進行視覺化評估，因此欠缺客觀性；最重要的是，去雲過程通常也會破壞原本的地物資訊，然而去雲後影像能否用來進行自動化地物判釋也欠缺探討。 為解決以上瓶頸，降低雲層的影響並提升地物判釋的正確性，就單時期具有厚雲層的影像而言，本研究以標準差延伸加強 (standard deviation stretch enhancement) 進行影像處理，再以區域增長 (region growing) 之方式偵測並切除無法還原地物資訊的厚雲層。單時期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，雖然傅利葉分析的方法在模式建立階段仍需兩時期影像，但建立後的模式在對其它影像進行去雲處理時則僅需單時期資訊。而去雲結果的量化評估，厚雲方面以專家法評估偵測去除的範圍準確性，薄雲方面則以影像分類法以及常態化差異植被指數 (normalized difference vegetation index, NDVI) 評估雲下地物資訊還原的程度以及非雲下地物資訊的被破壞程度。 本研究證明了僅以綠、紅、近紅外波段且沒有無雲參考區或參考影像時，對於厚雲偵測來說資訊量是足夠的，在不同特性的研究區，整體精度皆可達到90%以上。而對薄雲去除而言，三個波段在視覺上能達到一些改善的效果，對於地物光譜資訊還原方面，就全幅影像來探討，薄雲過濾器提升了約4%的分類精度，而就各分區來探討，過濾器對雲區的分類精度提升最多，達到了6%，無雲無影區亦有少許提升，影區的分類精度則反而下降，雖然薄雲過濾器無法全面提升影像各區之分類精度，然而其去雲的功效已有發揮。而薄雲過濾器也減輕了薄雲對NDVI值的影響，使其接近無雲狀態下的地物光譜資訊。總體來看，薄雲過濾器對影像分類以及NDVI值的改善程度而言在統計上有達到顯著性 (p < 0.01)。本研究之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，除能減少人工判釋和去除雲層的人力，也可增加衛星影像的利用度。

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.