基于NPP/VIIRS夜光遥感影像的作业灯光围网渔船识别

为对远洋灯光渔船作业信息进行实时动态监测,该研究基于可见光红外辐射仪(visible infrared imaging radiometer suite,VIIRS)夜光遥感影像,根据远洋灯光渔船作业时其集鱼灯灯光在VIIRS白天/夜晚波段(day/night band,DNB)影像上的辐射特征,采用峰值中值指数(spike median index,SMI)对灯光渔船与背景像元间的辐射差异进行拉伸,在此基础上设计了基于最大熵法(maximum entropy method,Max Ent)阈值分割以及局部峰值检测(local spike detection,LSD)的作业远洋灯光渔船识别算法,并采用2015年西北太平洋公海灯光围网渔场内作业渔船船位监控系统(vessel monitoring system,VMS)数据对该算法的识别精度进行检验。验证结果显示,该文提出的作业远洋灯光渔船自动识别算法对实际作业灯光渔船的识别精度在92%以上,可以满足远洋灯光渔船日常监测的需求,可为进一步评估远洋光诱渔业捕捞努力量、推进远洋光诱渔业信息化管理以及打击非法、未申报和无管制的(illegal,unregulated,unreported,IUU)捕捞活动提供技术支持。

Identification for operating pelagic light-fishing vessels based on NPP/VIIRS low light imaging data

Fishing data are the basement of fisheries science research, but currently the source of fishing data is extraordinarily scarce, and data quality is poor in some aspects. Satellite low light sensors can detect the light-fishing vessels at night, however, its application in pelagic fishery has been limited by the lack of an algorithm for extracting the location and brightness of operating pelagic light-fishing vessels. An examination of operating pelagic light-fishing vessels features in the day/night band (DNB) image, which was from the visible infrared imaging radiometer suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) satellite, indicated that the features were a list of nonadjacent bright spots. In order to identify the operating pelagic light-fishing vessels from VIIRS/DNB accurately, we designed a set of identification algorithm for operating pelagic light-fishing vessels according to the light radiation characteristics of its fishing gathering lamps in NPP/VIIRS low light image. Before applying the identification algorithm, a data pre-processing step was adopted through radiation stretch and noise reduction by adaptive Wiener filter to prepare the data for further analysis and use. A spike median index (SMI) was used to enlarge the radiation difference between operating pelagic light-fishing vessel pixels and background pixels. On the basis of this, an adaptive threshold segmentation method called the maximum entropy (MaxEnt) method was used to extract the bright spot pixels, and generated a list of candidate operating pelagic light-fishing vessels detections. The candidate pixels were then filtered to remove the false identification bright spot pixels distributed near the operating pelagic light-fishing vessel pixels, and illuminated by the high-power fishing gathering lamps by a local spike detection (LSD) algorithm. A validation study was conducted at a night with weak lunar illuminance on May 24, 2015 which was selected randomly, using the vessel monitoring system (VMS) data of Chinese operating light-seiners vessels on the high seas of Northwest Pacific Ocean light seine fishing ground and the result of VIIRS/DNB image visual interpretation. The validation result showed that the identification algorithm detected 27 operating pelagic light-fishing vessels on the high seas of Northwest Pacific Ocean light seine fishing ground, and the number of operating pelagic light-fishing boats and their distribution were entirely consistent with the result of VIIRS/DNB image visual interpretation; the VMS data had the record of 25 operating pelagic light-fishing vessels among the total 27 vessels, and their distribution was nearly the same with the result of identification algorithm and VIIRS/DNB image visual interpretation. The identification algorithm worked well when lunar illuminance was weak and its identification accuracy was above 92%. The identification algorithm not only avoided the subjectivity and uncertainty of certain threshold segmentation, but also removed the false identification bright spot pixels near the operating pelagic light-fishing vessel pixels, which were illuminated by the high-power fishing gathering lamps. Detection of operating pelagic light-fishing vessels based on VIIRS/DNB imaging data can provide up-to-date activity and change information of operating pelagic light-fishing vessels for pelagic light-fishing industry, which meets the need of fishing boat''s daily monitoring, and has a wide application prospect in fishing effort estimation, research of central fishing ground spatial-temporal distribution and change, and fishery forecast and management.