Applying Image Fusion to Integrate Radar Images and SPOT Multi-spectral Satellite Images for Forest Type Classification

應用影像融合技術整合雷達影像與SPOT多光譜影像於林型分類

張嘉豪(Chia-Hao Chang) ； 謝依達(Yi-Ta Hsieh) ； 吳守從(Shou-Tsung Wu) ； 陳朝圳(Chaur-Tzuhn Chen) ； 陳建璋(Jan-Chang Chen)

《台灣林業科學》 30卷3期 (2015/09) Pp. 201-209

https://doi.org/10.7075/TJFS.201509_30(3).0005

摘要

林型分類往往需要投入大量的人力與物力，利用遙測技術節省調查成本。遙感探測中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。傳統以遙測方法進行土地覆蓋分類多以光學影像為主，但常受限於天候及日夜因素無法得到較精確之結果，而合成孔徑雷達（synthetic aperture radar, SAR）主要使用微波（microwave）可不受上述因素之限制，具有研究發展的優勢與分類應用領域，許多研究指出光學影像與SAR融合進行土地覆蓋分類可以獲得更精準之資訊。本研究使用ALOS/PALSAR L波段，並透過光譜特性與表面粗糙度的資訊，提高分類準確度。本研究透過IHS轉換融合法及小波轉換融合法進行融合雷達影像及多光譜衛星影像，進行最大概似法（maximum likelihood method, MLC）進行林型分類，得到IHS轉換融合法分類準確度達83.86%、kappa值0.8152，小波轉換融合法準確度達72.68%、kappa值0.6889較原始SPOT影像分類準確度65.71%、kappa值0.6052為高，使影像分類準確度提升18%，展現出雷達影像及多光譜衛星影像之融合應用價值。

關鍵字

合成孔徑雷達 ； 多光譜影像 ； 影像融合 ； 林型分類

並列摘要

Forest type mapping requires considerable manpower and resources. Therefore, using remote sensing techniques to reduce resource requirements is common in forest inventories. Remote sensing includes active radar and passive optical sensors which can provide different kinds of information. Conventional remote sensing methods for forest type classification mainly use optical images, but they are affected by weather and day/night with the consequence that results are not always accurate. Synthetic aperture radar (SAR) relies on microwave radiation, and is not affected by the above restrictions. Some studies combine SAR and optical images to increase the accuracy of forest type classification. In this study, we used ALOS PALSAR L band images with a wavelength of 0.25 m. We used a combination of spectral features and roughness information to improve the classification accuracy. For analysis and processing, radar images and multispectral satellite images were combined using the intensity, hue and saturation (IHS) and wavelet transformation (WT). We used IHS images, WT images, and SPOT images to classify the forest types by the maximum likelihood method. Results showed that the overall accuracy was 83.86%, and the kappa value was 0.81 for IHS, and the overall accuracy was 72% and kappa value was 0.68 for WT. These results were better than those based on SPOT images, the overall accuracy of which was 65% and kappa value was 0.60. We found that combining SAR and optical images improved the accuracy by approximately 18%, thereby improving forest type classification.

並列關鍵字

synthetic aperture radar ； multi-spectral image ； image fusion ； forest type classification