Abstract
Historical aerial images are important to retain past ground surface information. The land-use land-cover change in the past can be identified using historical aerial images. Automatic historical image registration and stitching is essential because the historical image pose information was usually lost. In this study, the Scale Invariant Feature Transform (SIFT) algorithm was used for feature extraction. Subsequently, the present study used the automatic affine transformation algorithm for historical image registration, based on SIFT features and control points. This study automatically determined image affine parameters and simultaneously transformed from an image coordinate system to a ground coordinate system. After historical aerial image registration, the land-use land-cover change was analyzed between two different years (1947 and 1975) at the Tseng Wen River estuary. Results show that sandbars and water zones were transformed into a large number of fish ponds between 1947 and 1975.
Highlights
Land-use and land-cover change is a process for the human modification and biophysical attribute change of the Earth’s terrestrial surface [1]
Lowe (2004) first considered image matching and recognition based on the Scale Invariant Feature Transform (SIFT) algorithm [4]
This study was accomplished land-use land-cover analysis based on historical image registration and on-screen digitizing
Summary
Land-use and land-cover change is a process for the human modification and biophysical attribute change of the Earth’s terrestrial surface [1]. Most historical aerial images have difficulty in applying for environmental monitoring because the pose information of historical images is usually lost. Stitching the correct locations of historical aerial photos is a necessary process in multi-temporal image analysis and land-use land-cover change analysis [2,3]. Harris corner and Susan corner detections were used to identify classic point features. These features are sensitive to scale and rotation. SIFT can refine the weakness of area-based image-matching method and can provide good feature points for quick image stitching [8]
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