融合合成孔徑雷達影像與衛星遙測多光譜影像於林地覆蓋分類

Fusion of Synthetic Aperture Radar Image and Satellite Remote Sensing Multi-spectral Images on Forest Land Cover Classification

張嘉豪(Chia-Hao Chang)

指導教授 ： 陳建璋

國立屏東科技大學/農學院/森林系所/碩士(2014年)

https://doi.org/10.6346/NPUST.2014.00234

摘要

森林資源調查往往需要投入大量的人力與物力，利用遙測技術配合地面調查可節省調查成本與提升效率。遙感探測之感測器主要分為主動式雷達感測器和被動式光學感測器，兩種影像擁有不同的資訊，因此整合兩種不同類型之遙測影像，將有利於資源調查之應用，例如環境變遷、災害監測、地物分類和植生復育等。傳統以遙測方法進行土地覆蓋分類多以光學影像為主，但常受限於天候不佳或夜間無法取得影像之因素，合成孔徑雷達(Synthetic Aperture Radar, SAR)則使用微波遙測，可不受上述因素之限制，具有研究發展優勢，融合光學影像與SAR進行林地覆蓋分類，是否可提升分類準確度為本研究之主要目的。本研究利用ALOS/PALSAR L波段與波長0.25 m影像與SPOT-4衛星遙測多光譜影像，並透過光譜特性與表面粗糙度的資訊，試圖提高分類準確度。本研究透過ERDAS IMAGINE 9.2軟體進行雷達影像分析與處理，結果顯示Gamma濾波器較李式濾波器及Frost濾波器效果良好，為最佳之濾波方法。影像融合方面透過IHS轉換融合法、主成分分析融合法及小波轉換融合法進行融合合成孔徑雷達影像及多光譜衛星影像。分類以最大概似法(Maximum Likelihood Method, MLC)進行森林土地利用類型分類，得到IHS轉換融合法分類準確度達83.86%、Kappa值0.8152；主成分分析融合法準確度達82.44%、Kappa值0.7989；小波轉換融合法準確度達72.68%、Kappa值0.6889較原始SPOT-4影像分類準確度達65.71%、Kappa值0.6052，使影像分類準確度提升18.15%，顯示融合雷達影像及多光譜衛星影像有利於提高林地分類之準確度。

關鍵字

影像融合 ； 林地覆蓋分類 ； 光學影像 ； 斑駁雜訊 ； 合成孔徑雷達

並列摘要

Forest inventory requires considerable manpower and material resources. Therefore, using the remote sensing techniques and field surveying to reduce inventory cost and promote the efficiency is a common method in forest inventory. Remote sensing includes active radar sensor data and passive optical sensor data. These sensors provide different information, therefor integration two different type images which can be beneficial for many applications, such as environmental change research, disaster monitoring, land cover classification, and vegetation regeneration. Conventional telemetry methods for land cover classification are mainly in optical images, but are affected by weather and night that cannot to acquire image data. Synthetic Aperture Radar (SAR) relies on microwave radiation, and is not affected by the weather and night. Combining SAR and optical images to promote the forest land cover classification accuracy is main purpose of this study. In this study, we used the ALOS PALSAR L band images with a wavelength of 0.25 m and SPOT-4 multispectral satellite images. To combinating the spectrum features and roughness information, to improve classification accuracy. We use ERDAS IMAGINE 9.2 software to analyze and the process of SAR image. The result showed that Gamma filter was better than Lee filter and Frost filter. SAR images and multispectral satellite images were combined by Intensity, Hue and Saturation (IHS), Principal Component Analysis (PCA) and Wavelet Transformation (WT). We used IHS images, PCA images, WT images and SPOT-4 images to classify the forest land cover classification using Maximum Likelihood Method (MLC). The results showed that overall accuracy was 83.86% and Kappa was 0.8152 in IHS, overall accuracy was 82.44% and Kappa was 0.7989 in PCA and overall accuracy was 72.86% and Kappa was 0.6889 by WT. These results are better than those based on SPOT-4 images whose overall accuracy was 65.71% and Kappa was 0.6052. Results indicate that the fusion of SAR and optical images will improve the classification accuracy by approximately 18.15%, thereby improving forest land cover classification.

並列關鍵字

Image fusion ； Forest land cover classification ； Optic image ； Speckle noise ； Synthetic aperture radar

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