Abstract
This paper presents an automated image registration approach to detecting changes in multi-temporal remote sensing images. The proposed algorithm is based on the scale invariant feature transform (SIFT) and has two phases. The first phase focuses on SIFT feature extraction and on estimation of image transformation. In the second phase, Structured Local Binary Haar Pattern (SLBHP) combined with a fuzzy similarity measure is then used to build a new and effective block similarity measure for change detection. Experimental results obtained on multi-temporal data sets show that compared with three mainstream block matching algorithms, the proposed algorithm is more effective in dealing with scale, rotation and illumination changes.
Highlights
As an advanced detection technology, remote sensing has been widely applied in many areas[1]
Since the 1990s, scientists have made remarkable achievements in change detection techniques, including context-sensitive method[2], GSM approach to automatic change detection in multi-temporal SAR images[3], ratio analysis[4], standardized processing[5], registration noise reduction[6], MTF-based change detection analysis[7], automatic unsupervised change detection based on a RGM distribution[8], etc
We proposed SLBHP (Structured Local Binary Haar Pattern) 23 that modified from LBP with Haar wavelet for pixel-based graphics retrieval
Summary
As an advanced detection technology, remote sensing has been widely applied in many areas[1]. Since the 1990s, scientists have made remarkable achievements in change detection techniques, including context-sensitive method[2], GSM approach to automatic change detection in multi-temporal SAR images[3], ratio analysis[4], standardized processing[5], registration noise reduction[6], MTF-based change detection analysis[7], automatic unsupervised change detection based on a RGM distribution[8], etc. It is well known that remote sensing images always acquired through different viewpoints, times and sensors These factors may result in rotation, illumination changes or even affine transformation in multi-temporal images. Experiments carried out on multi-temporal remote sensing images confirm that the improved change detection algorithm is invariance to scale, rotation, and illumination transformations.
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