## Yan Bian , Yusheng Gong , Guopeng Ma and Ting Duan## |

Number of study areas | Imaging time | Image size (pixel × pixel) |
---|---|---|

Island 1 | 2019-6 | 27620×35273 |

Island 2 | 2019-6 | 4382×4986 |

Island 3 | 2019-7 | 16583×19674 |

Island 4 | 2019-8 | 25769×33748 |

In this study, qualitative and quantitative indicators were used to compare and analyze the final extraction results of island water edges. Among them, qualitative analysis refers to the superposition and comparative analysis of the final extraction result of water boundary with the original image and reference image in Fig. 5. The quantitative index is evaluated by three evaluation indexes, namely accuracy P, omission error Q and redundancy error R, respectively [13], and the quantitative analysis is conducted. The equation is expressed as follows:

In Eqs. (4)–(6), i represents the vector length of the water edge line of the island which was drawn manually in the reference image; a represents the total length of the overlapping part between the extracted water boundary line and the manually drawn water boundary line vector; b refers to the total length missing from the extracted water edge compared with the artificially drawn vector, that is, the length not extracted when the water edge is taken as the background noise; c refers to the total length of redundancy of the extracted water edge compared with the manually drawn vector, that is, the background noise is taken as the extracted length of the water edge.

The experiment is based on Windows Server 2016 operating system, with 16G internal storage, a NVIDA Quadro P2000 video card and 5G video storage. The program development language is MATLAB2020b.

GA-OTSU threshold segmentation algorithm is the most critical step in GA method, and the determination of segmentation threshold is a key factor affecting the image segmentation effect. GA-OTSU threshold segmentation algorithm takes OTSU algorithm as the basic segmentation method, on which genetic algorithm is introduced to improve OTSU algorithm. Compared with the OTSU algorithm, the GA-OTSU threshold segmentation algorithm has the advantage of quickly and accurately obtaining the global optimal solution, that is, the optimal segmentation threshold t*, and reducing the noise. This experiment in segmentation algorithm, for the same data set, the GA-OTSU segmentation algorithm in the GA method in this paper is not only compared with the OTSU segmentation algorithm in [5], but also compared with the watershed segmentation algorithm in [4]. In order to visually display the segmentation effects of the four research areas, the comparative results of the three segmentation methods are presented, as shown in Fig. 6.

It can be seen from Fig. 6 that the watershed segmentation algorithm in [4], the traditional OTSU algorithm in [5], and the GA-OTSU segmentation method in this paper can segment the experimental area well, and the segmenting image can retain the original image feature information rather integrally. However, it is obvious that the GA-OTSU segmentation method is better than the traditional OTSU algorithm. It can not only complete image segmentation, but also clear separation between the target and the background. The final segmented image is clearer, with less noise and less information loss, which can more effectively separate target from background and highlight the areas of interest. It is also obvious that compared with the watershed segmentation algorithm, the GA-OTSU segmentation method almost does not have excessive segmentation, which can lay a good foundation for the subsequent extraction of water edges and indirectly improve the accuracy of water edges extraction.

The GA-OTSU segmentation algorithm can not only effectively segment the target image, but also find the most adaptive threshold of the image and improve the performance of image segmentation by calculating the fitness of the genetic algorithm to obtain the operation characteristics of the optimal solution. In the parameter setting of GA-OTSU segmentation algorithm in this experiment, the initial population number N is 8, and the maximum number of evolutionary iterations is 100. As can be seen from the variation diagram of the optimal fitness curve in Fig. 7(b), the ordinate value of the curve gradually increases and finally tends to be stable, which indicates that the optimal fitness value under this condition is found through the genetic algorithm, that is, the optimal solution of the threshold value obtained by the OTSU method is found. The optimal threshold generation graph in Fig. 7(a) shows that the stable value of the curve is the optimal threshold that meets the conditions. The optimal adaptive threshold graph obtained by watershed algorithm, the OTSU algorithm and the GA-OTSU segmentation method for island images in the four experimental areas is shown in Table 2. The GA-OTSU segmentation method can more accurately find the global optimal segmentation threshold of the target image to achieve the optimal segmentation effect, which is conducive to the next step of image processing.

Table 2.

Study areas | OTSU optimal threshold | Watershed optimal threshold | GA-OTSU optimal threshold |
---|---|---|---|

Island 1 | 105 | 100 | 96 |

Island 2 | 88 | 83 | 79 |

Island 3 | 91 | 87 | 84 |

Island 4 | 94 | 92 | 90 |

The binary image segmented by GA-OTSU is used to perform morphology closing operation with disk structure elements to fill in the holes. The results of morphological closure operation and the size of structural elements are shown in Fig. 8.

The Sobel edge detection operator is used to extract the edge of the closed binary image. Since the binary image processed by closed operation only has target (island) and background (ocean), the Sobel edge detection operator is used for boundary extraction, which is faster and has better effect, to realize the last step of GA island water edge automatic extraction method proposed in this paper, and the final island water edge is obtained. Superposition contrast analysis were also conducted between the results of water edges extracted by the GA method in this paper (GA-OTSU threshold segmentation algorithm + morphological closed operation + Sobel edge detection operation), the method in [4] (watershed segmentation + morphology modification method), the method in [5] (top-hat transform + OTSU segmentation + expansion and corrosion morphology method), the original and the reference images in Fig. 5. As shown in Fig. 9.

According to the qualitative comparative analysis of the extraction results in the first three experimental areas of the three methods in Fig. 9, it can be found that the water boundary extracted by the method of [4] (watershed segmentation + morphological modification) is over-extracted. The water edge extracted by the method in [5] (top hat transform + OTSU segmentation + expansion and corrosion morphology method) has obvious sawtooth phenomenon caused by the expansion and corrosion operation of morphology. The water edge extracted by GA method in this paper is integrated and smooth without fracture. In addition, the water edges extracted by the three methods were superimposed on the original image and the reference image (manually drawn vector), respectively. It can be found that the water edges extracted by the method in this paper also have the best matching effect with the original image and the reference image. The extraction results of the three methods are good in the fourth island, because the sea and land of which are distinct and the background is simple.

In addition to the above qualitative analysis, this paper also adopts the three quantitative evaluation indexes (accuracy P, omission error Q and redundancy error R) in Eqs. (4)–(6) above through the intersection tabulating tool in ArcGIS software to evaluate the accuracy of the island water boundary lines extracted by the three methods, the statistical results of extraction accuracy of the three methods are shown in Table 3.

It can be found from Table 3 that the average extraction accuracy of GA method in this paper can reach 98%, and the omission error (Q) and redundancy error (R) are 2.5% and 1.5%, respectively. The accuracy is significantly higher than the other two comparison methods.

Table 3.

Study areas | GA method | Method of [4] | Method of [5] | ||||||
---|---|---|---|---|---|---|---|---|---|

P | Q | R | P | Q | R | P | Q | R | |

Island 1 | 96.34 | 3.66 | 2.31 | 95.46 | 4.54 | 4.98 | 93.46 | 6.54 | 8.32 |

Island 2 | 97.62 | 2.38 | 1.02 | 97.03 | 2.97 | 3.65 | 94.62 | 5.38 | 6.43 |

Island 3 | 98.97 | 1.03 | 1.15 | 97.15 | 2.85 | 3.47 | 97.63 | 2.37 | 3.04 |

Island 4 | 99.24 | 0.76 | 0.87 | 98.64 | 1.36 | 1.95 | 98.01 | 1.99 | 2.43 |

The method proposed in this paper is compared with the methods in [4] and [5] through qualitative and quantitative analysis, and found that it is effective to extract the water edges of islands from GF-2 remote sensing images. The innovation of the GA method in this paper is partly reflected in the GA-OTSU segmentation method, which has the following advantages compared with the watershed segmentation algorithm in [4] and the OTSU segmentation algorithm in [5]: (1) it can better retain the edge of the segmented image and make the edge more integrated; (2) it has less noise; (3) there is basically no excessive segmentation phenomenon. Another innovation of the GA method is reflected in the com¬bination of GA-OTSU segmentation algorithms, morphological closure operation and Sobel edge detec¬tion operator, which has realized the automatic water edge extraction of islands, and ideal results with higher precision have been obtained, so the method can give reference advice for the water edge auto¬matic extraction of islands from GF-2 remote sensing image with high resolution in three bands.

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