Study On Edge Detection Algorithm Of Oil Slick Remote Sensing Image On The Sea

With the current maritime cause, offshore oil development and rapid economic development along the coast, oil spills accidents and illegal oily discharge occur frequently on the sea, and these situations represent a serious threat to the marine environment and cause great losses of energy sources. Early detection, monitoring, containment, and cleanup of oil spill are crucial for the protection of the environment. It is possible to monitor and identify oil spill with the rapid development of remote sensing technology, and many image processing techniques of remote sensing have came into being. In particular, the technology of edge detection is an important tool for the location and acreage calculation of oil slick on the sea by aerial remote sensing. Whenever we need to identify oil spill, confirm the location or get the shape and acreage of oil spill, we have to get the edge information of oil slick images firstly. Due to the complexity of oil spill remote sensing image, it is very difficult to gain accurate edge detection results by conventional edge detection methods. Therefore, further research is still needed. In this research, we mainly focus on the edge detection of the oil spill remote sensing images, and put forward three innovative edge detection algorithms, the main works in this thesis can be summarized as follows:1. Considering conventional edge detection methods are focused on three main problems:determination of candidate edge points, threshold denoising and edge linking, we propose a new edge detection method based on a dynamic block threshold denoising algorithm and an improved GDNI edge linking algorithm. Comparing with the current common global threshold algorithm, the proposed dynamic block threshold algorithm considers the local information of edge gradient and avoids the problems while using global threshold, thereby determining the true edge points more accurately. In the process of edge linking, we propose an improved GDNI edge linking algorithm which uses a cost function based on the weighting combination of Euclidean distance, intensity information and angle information of edge ending points and finally to improve the edge linking decision. We demonstrate through several experiments that the combination of the two algorithms achieves good edge detection results for oil slick remote sensing images with low contrast and weak noise, and has a better real-time performance.2. A novel global active contour edge detection model (RSF-GAC) based on region scalable fitting is proposed, which makes full use of advantages of RSF model and GMAC model. RSF-GAC model introduces the edge information of image and local region information under the framework of GMAC, so RSF-GAC model can avoid the existence of local minima and meanwhile deals with the intensity inhomogeneity, noise, and weak edge boundaries exiting in images. In the process of the active contour evolving toward object boundaries and numerical minimization, a dual formulation based on the weighting total variation is used for converting the minimization problem of RSF-GAC into an iterative process and overcoming drawbacks of curve evolution method based on the usual level set and gradient descent method so that the process of minimization can be much easier and and our algorithm is independent of the initial position of the contour. The numberical iterative process has been described literally. Large numbers of experiment results have shown that the proposed RSF-GAC model outperforms other algorithms in terms of the efficiency and accuracy with satisfactory results of edge extraction for oil slick images with low contrast, strong noise and weak intensity inhomogeneity.3. Oil slick remote sensing images ususlly have a serious problem of intensity inhomogeneity. The existing active contour models can not deal with the problem very well, so a robust active contour model is proposed based on the local Gaussian fitting and the correction of intensity inhomogeneity. Firstly, we describe the image with intensity inhomogeinty by using a common mathematical model, and try to establish a region energy model based on the local Gaussian distribution. Finally a novel and robust LGF-IHC edge detection model is constructed by combining the established region energy model and the geodesic active contour model. In the process of seeking the minimum solution of LGF-IHC energy model, we use the Chan's global minimum optimization theory. In the process of the active contour evolving toward object boundaries and numerical minimization, a dual formulation based on the weighting total variation is used and implements global minimization iteration of the LGF-IHC model fast and stably. Large numbers of experiment results have shown that the proposed LGF-IHC model can be robust to the high noise and severe intensity inhomogeneity existing in oil slick remote sensing images. In addition, the accurate edge detection of oil slick region and the correction of intensity inhomogeneity are simultaneously achieved via the proposed LGF-IHC model. Compared with the dynamic block threshold denoising algorithm and an improved GDNI edge linking algorithm and the RSF-GAC model, LGF-IHC edge detection model has higher accuracy and robustness, furthermore, it will have a better application prospect in the edge detection of oil slick remote sensing image.