| 中文摘要 |
Agricultural land is important for the food security of a nation. However, the total agriculture land area across the world has been decreasing every year due to human activities. Developing technology, especially in businesses and other industries, is resulting in the increased construction of buildings, which then results in loss of land. People build factories, houses, etc. on agricultural areas, which then become impervious areas as the water absorption ability of the ground underneath decreases. A periodical assessments to record the change in the total farmland area need to be carried out. Traditional manual digitation is usually conducted to detect impervious areas in agricultural land. However, this process is laborious. Thus, this study uses an object-based approach that employs high-resolution satellite images to detect the impervious areas. A pan-sharpened Pleiades image with 0.5-meter resolution and four spectral bands were utilized. The HSV（hue-saturation-value）bands derived from the RGB bands were added as object features to extract the impervious area. The spectral feature, i.e., HSV, NDVI, NDWI, the soil extraction algorithms, and the shape feature, i.e., size and compactness, were deployed to extract the impervious area within the agricultural land. An F1-score of 0.70 was obtained from this proposed method. Furthermore, the transferability test was carried out by applying the method to different image size and different image acquisition time. The result shows that the method is stable to process various image scenes. |
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