Edge detection is a crucial method for the location and quantity estimation of oil slick when oil spills on the sea. In this paper, we present a robust active contour edge detection algorithm for oil spill remote sensing images. In the proposed algorithm, we define a local Gaussian data fitting energy term with spatially varying means and variances, and this data fitting energy term is introduced into a global minimization active contour (GMAC) framework. The energy function minimization is achieved fast by a dual formulation of the weighted total variation norm. The proposed algorithm avoids the existence of local minima, does not require the definition of initial contour, and is robust to weak boundaries, high noise and severe intensity inhomogeneity exiting in oil slick remote sensing images. Furthermore, the edge detection of oil slick and the correction of intensity inhomogeneity are simultaneously achieved via the proposed algorithm. The experiment results have shown that a superior performance of proposed algorithm over state-of-the-art edge detection algorithms. In addition, the proposed algorithm can also deal with the special images with the object and background of the same intensity means but different variances.
An illumination and affine invariant descriptor is proposed for registering aerial images with large illumination changes and affine transformation, low overlapping areas, monotonous backgrounds or similar features. Firstly, triangle region is detected by K-nearest neighbors (K-NN) graph of initial matched result by Scale-Invariant Feature Transform (SIFT). In order to improve the accuracy, region growth is applied to boost small and slender triangles. Then illumination and affine invariant descriptor is defined to describe triangle regions and measure their similarity. The descriptor named as IIMSA is the combination of MultiScale Autoconvolution (MSA) and multiscale retinex (MSR). The performance of the descriptor is evaluated with optical aerial images and the experimental results demonstrate that the proposed descriptor IIMSA is more distinctive than MSA and SIFT.
Accurate point matching is a crucial and challenging process in feature-based image registration, especially for
images with a monotonous background, In this paper, we propose a robust point matching algorithm for image
registration which integrates cyclic string matching method and a two decision criteria, i. e., the stability and accuracy of
transformation error. In this algorithm, a filtering strategy is designed to eliminate dubious matches to get exactly
matched point sets. The performance of the proposed algorithm is evaluated by registering two typical image pairs
containing repetitive patterns. Compared with Random Sample Consensus (RANSAC), Graph Transformation Matching
(GTM), the proposed algorithm obtains the highest precision and stability.
Spilled oil is one of the most serious marine environment disasters, which damaged ecological environment seriously
with long-term and large-scale impact. Based on the experiment and research in the Canadian Centre of Environmental
Technology, an experiment is taken to detect the underwater suspended oil-spills by Laser-induced fluorescence. It
quantizes the conditions that Laser-induced fluorescence can be used to detect underwater oil, and makes a solid theory
foundation for the system design of underwater oil detection by Laser-induced fluorescence. This environmental solves a
key problem for underwater oil detection by Laser-induced fluorescence.
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