融合悬浮颗粒物自适应分区的渤海一年生海冰自动检测方法

邱华昌; 宫兆宁; 赵宇欣; 伍洪委

doi:10.11834/jrs.20254378

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融合悬浮颗粒物自适应分区的渤海一年生海冰自动检测方法
Automatic detection algorithm of annual sea ice in Bohai Sea incorporating adaptive partitioning of suspended particulate matter
2025年页码：1-19
网络出版：2025-10-28，
DOI： 10.11834/jrs.20254378
稿件说明：

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邱华昌，宫兆宁，赵宇欣，伍洪委.XXXX.融合悬浮颗粒物自适应分区的渤海一年生海冰自动检测方法.遥感学报，XX（XX）： 1-19 DOI： 10.11834/jrs.20254378.

Qiu Huachang，Gong Zhaoning，Zhao Yuxin，Wu Hongwei. XXXX. Automatic detection algorithm of annual sea ice in Bohai Sea incorporating adaptive partitioning of suspended particulate matter. National Remote Sensing Bulletin， XX（XX）：1-19 DOI： 10.11834/jrs.20254378.

摘要

一年生海冰是中纬度气候变化的重要指示因子，其中厚度小于10 cm的薄冰对气候变化的响应尤为显著。针对渤海高动态悬浮泥沙海域中海冰光谱波动性强、反射率变化显著、薄冰检测难度大的问题，本文提出一种基于光谱形状特征的自适应分区方法，将渤海动态划分为低、高悬浮颗粒物浓度区域。分区后，区域内悬浮颗粒物浓度的异质性显著降低，有效提升了厚度10 cm以下薄冰的可检测性。在此基础上，采用单峰阈值法自动确定分割阈值，并融合边缘特征以增强算法的稳健性。将该方法应用于MODIS、Sentinel-2、GF-1、Sentinel-3及GOCI五种光学影像数据，并利用自然资源部北海预报中心发布的2017-2019年共12景海冰解译图及高分辨率遥感影像样本点进行精度验证。结果表明：该算法分类精度超过90%，适用于多种光学传感器；通过光谱线性混合模型模拟实验进一步证实，该算法能在高动态悬浮泥沙海域有效识别密集度高于30%的一年生海冰。本研究为多源光学遥感数据的业务化海冰监测提供了有效方法支撑。

Abstract

ObjectiveAnnual sea ice is an important indicator of climate change in mid-latitudes

especially the thin ice with a thickness of less than 10 cm has a more significant response to climate. The Bohai Sea

as a marine system with special research value in the temperate monsoon climate zone and even globally

shows a high degree of sensitivity in its natural ecological and socio-economic systems

and there is a significant bidirectional feedback effect between the sea ice dynamics and the regional climate and human activities. The Bohai Sea is affected by forty runoffs from the Yellow River

Liao River

and Hai River

and the concentration of suspended particulate matter (SPM) in seawater is much higher than that in other sea areas

and the high dynamics of suspended sediment also leads to the complexity of the spectra of sea ice and seawater

which increases the difficulty of accurately detecting the extent of sea ice in the Bohai Sea.MethodIn this study

a segmented processing strategy was employed. Initially

sea ice with a thickness greater than 10 cm was extracted by means of a simple threshold segmentation method. Secondly

in order to address the challenges posed by the high spectral volatility of sea ice and the difficulty in detecting thin ice in the highly dynamic suspended sediment sea area of the Bohai Sea

this study proposes an adaptive partitioning method based on spectral shapes by deeply analysing the spectral characteristics of ice and water in this region. The method is predicated on the dynamic division of the Bohai Sea into regions characterised by low and high suspended particulate matter concentrations. Following this treatment

the spatial heterogeneity of SPM concentration in the region is significantly reduced

thereby effectively improving the detectability of thin ice with a thickness of less than 10 cm. Subsequently

an analysis was conducted on the four bands most commonly employed in optical images

with a view to ascertaining their degree of separability and identifying the preferred segmentation features. The analysis results demonstrate that the blue band and the near-infrared band are the most effective segmentation bands for low and high SPM concentration regions

respectively. The segmentation threshold is determined automatically based on the preferred features using the single-peak threshold method

and the image edge features are fused to enhance the robustness of the algorithm.ResultThe present method is applied to five optical images

MODIS

Sentinel-2

GF-1

Sentinel-3 and GOCI

and the accuracy is verified by using the 12 views of sea ice interpretation maps and sample points of high-resolution remote sensing images for the years 2017-2019 released by the North Sea Forecasting Centre of the Ministry of Natural Resources. The results show that the accuracy of this algorithm can reach more than 90% and is applicable to a variety of optical images; simulation experiments using the spectral linearity mixing model demonstrate that this algorithm is capable of identifying annual sea ice with densities of more than 30% in highly dynamic suspended sediment sea areas.ConclusionThis method can automatically

stably

efficiently and accurately extract annual sea ice

which can be applied to multi-source sensors

and also provides data support for researching climate change by giving full play to the advantages of multi-source remote sensing data for more comprehensive and fine sea ice monitoring.

关键词

Keywords

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