Fusion of Synthetic Aperture Radar Image and Satellite Remote Sensing Multi-spectral Images on Forest Land Cover Classification
| Autor: | Chang, Chia-Hao, 張嘉豪 |
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| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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