遥感技术支持下海岸线提取算法.docx

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Title: Coastal Line Extraction Algorithm based on Remote Sensing Techniques
Abstract:
In recent years, the accurate extraction of coastal lines has become a crucial task for various applications such as coastal planning, environmental management, and erosion analysis. This paper aims to present an algorithm for coastal line extraction using remote sensing techniques. The proposed algorithm combines image segmentation, edge detection, and region-growing methods to achieve an accurate and efficient extraction of coastal lines. Experimental results demonstrate the effectiveness of the algorithm in extracting coastal lines from satellite images.
1. Introduction
Coastal zones are ecologically and economically significant areas that are prone to erosion and subjected to dynamic changes. Monitoring and analyzing coastal lines are essential for effective coastal resource management. Remote sensing techniques have been widely used in coastal analysis due to their ability to provide large-scale, repetitive, and time-series data. This paper addresses the challenge of extracting coastal lines from remote sensing imagery.
2. Existing Methods
Several existing methods have been proposed for coastal line extraction. Some methods utilize edge detection algorithms, such as the Canny edge detector and the Sobel operator. Others rely on image segmentation techniques, including thresholding, region growing, and clustering algorithms. However, these methods often suffer from limitations in accuracy and efficiency when applied to the extraction of coastal boundaries.
3. Proposed Algorithm
The proposed algorithm combines the strengths of image segmentation, edge detection, and region-growing methods to improve the accuracy and efficiency of coastal line extraction. The algorithm can be summarized as follows:
Preprocessing
- Satellite imagery is preprocessed to remove noise, enhance contrast, and improve image quality.
- The coastline region of interest is selected based on geographical information and user-defined inputs.
Image Segmentation
- Otsu's method is applied to perform global thresholding on the preprocessed image.
- To overcome challenges such as varying illumination and color variations, further segmentation is performed using region-growing techniques.
Edge Detection
- Canny edge detection algorithm is applied to detect strong edges in the segmented image.
- Spatial and temporal filters are implemented to remove false edges caused by noise and artifacts.
Coastal Line Extraction
- Edge pixels within the coastal region of interest are grouped using a region-growing algorithm.
- The extracted coastal line segments are connected to form a continuous boundary.
4. Experimental Results
To evaluate the performance of the proposed algorithm, multiple satellite images of coastal areas were used. A quantitative analysis was conducted by comparing the extracted coastal lines with ground truth data obtained from manual digitization. The results demonstrate the effectiveness of the algorithm in accurately extracting the coastal lines with an overall accuracy of X%.
5. Conclusion
This paper presented a coastal line extraction algorithm based on remote sensing techniques. The proposed algorithm combines image segmentation, edge detection, and region-growing methods to overcome the limitations of existing methods. Experimental results indicate that the algorithm achieves high accuracy in extracting coastal lines from satellite images. The algorithm has the potential to be integrated into coastal management systems for effective coastal planning, erosion analysis, and environmental management. Further research can explore the scalability and applicability of the algorithm to different coastal environments and sensor types.