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Automatic extraction of coastline via spectral angle-distance similarity growth model

  • role： First author第一作者
  • Affiliation：

    Geological Survey of Jiangsu Province, Nanjing 210049, China

    Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing 100101, China

  • Email：zytayx@sina.com
  • Introduction：詹雅婷(1985—　)，女，工程师，研究方向为海岸带遥感信息提取。E-mail：zytayx@sina.com
ZHAN Yating12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Geological Survey of Jiangsu Province, Nanjing 210049, China

  • Email：zlpinan@163.com
  • Introduction：朱利(1978—　)，男，高级工程师，研究方向为水色遥感应用。E-mail：zlpinan@163.com
ZHU Li3*,  
  • Affiliation：

    Geological Survey of Jiangsu Province, Nanjing 210049, China

SUN Yonghua4,  
  • Affiliation：

    Geological Survey of Jiangsu Province, Nanjing 210049, China

SU Xiaobei5,  
  • Affiliation：

    Geological Survey of Jiangsu Province, Nanjing 210049, China

HOU Haiqian6

resumen

Coastline change is a major indicator of alterations in the coastalecosystem and has been the focus of attention in marine economy. In this study, the spectral angle-distance similaritygrowth model was established to address the feasibility of automatic coastline extraction HJ-1B/infrared scanner (IRS) and the instability problem of the threshold value of traditional methods that use data at different phases. This model broadens the application and increases the value of IRS. The spectralangle-distance similarity growth modelutilizes the normalized radiation values of multispectralpixels as vector elements, which measure the anglesimilarity of different multidimensional vectors in the distance space. This model also analyzes the angle-distance similarity of water sample pixels and eight neighborhood pixels through iterative calculation. The water-land boundary was determined with the regional growth of water sample pixels via similarity constraint. The analysis of channel radiometric normalization showed that normalized channel values with reflectance and maximum quantization level reflect landmark change with time. The sample similarity analysis showed that the model was suitable forwhole-phase coastline extractionby taking two time standard deviation (0.01) of similar water bodies and other landmarksas the growth threshold (0.98), and that the general accuracy was more than 80%. Verification data analysis indicated that the threshold of the angle-distance similarity growth model was stable and was unaffected by phase. Comparing the extraction results of digital image edge detection methods, such as high-pass Filter, Roberts filter, Fast Fourier Transform (FFT), and Canny algorithm, revealed that these methods caused numerous broken stripes and spots in a complicated topography area. The extraction result of the waterlineshowed a mixture of numerous cracks and gaps, which hindered the further processing of the coastline. FFT was not applicable for coastline extraction through IRS given that the influence of stripe noises. Moreover, the coastline cannot be extracted through high-pass and Roberts filters, except for Canny algorithm, when the noise of IRS B4 was normal. However, Canny algorithm cannot be used when the noise was non-normal. These algorithms extracted coastlines with multi-inland border lines, which were difficult to remove. The effective removal of inland border lines from the extraction result was a difficult problem for these algorithms. By comparison, the angle-distance similarity model can be applied to coastline extraction viaIRS sensor, which was not sensitive to the noise sensor. It can also obtain desired results when the noise of B4 was non-normal. Furthermore, the subsequent processing was simple because no inland border lines interfered. This model enhances the application value of the IRS sensor in coastline extraction. The cloud cluster covered both land- and sea-occluded spectrum information, which led to ineffective coastline extraction. Therefore, data selection is necessary before application. The coastline, which is extracted from remote sensing images in this study, only included the instantaneous waterline and not the coastline in the strict sense. Artificial coastline and bedrock shoreline can be directly used as shoreline. However, the final extraction result of gravel coastlines, mangroves, and estuary coastlines need to be modified using tide and digital elevation model data.

palabra clave

coastline;angle-distance similarity;IRS;remote sensing

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