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| 1 | 基于HY-2A/SCAT数据极地海冰检测方法研究显示文摘本文基于HY-2A/SCAT数据,采用贝叶斯算法、线性判别算法、支持向量机算法、基于主成分分析(Principal component Analysis,PCA)的BP神经网络算法对极地地区的海冰进行检测,并将检测结果与SSMIS海冰密集度数据进行比较。结果表明:四种检测算法得到的海冰边界介于SSMIS 0%~30%海冰密集度边界之间。在高风速条件下,海冰和海水的后向散射特征区分不明显可能造成冰水误判,以2013年9月16日北极海冰检测为例,贝叶斯算法检测结果误判最少,其次为基于PCA的BP神经网络算法,线性判别和支持向量机两种算法误判率较高。考虑到检测算法的运行效率和冰水误判率,选择贝叶斯算法和支持向量机算法进行海冰面积的季节趋势分析,两种算法得到的海冰面积变化趋势都能反映季节性变化,且在海冰生长季支持向量机算法探测的海冰面积与SSMIS 15%密集度海冰范围保持较好的一致性。 | 赵朝方 徐锐 赵可 | 2019 | 中国海洋大学学报(自然科学版)2019，49，10： | 4 |
| 2 | Polar Sea Ice Identification and Classification Based on HY-2A/SCAT Data显示文摘In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification. | XU Rui ZHAO Chaofang ZHAI Xiaochun ZHAO Ke SHEN Jichang CHEN Ge | 2022 | Journal of Ocean University of China2022，21，2： | 0 |
| 3 | 基于CFOSAT散射计的海冰识别方法研究显示文摘中法海洋卫星散射计(CSCAT)丰富的观测几何信息为极地海冰遥感提供了新的机遇。本文提出一种适用于CSCAT的贝叶斯海冰识别算法,不需要构建海冰地球物理模式函数和计算后向散射系数离海冰地球物理模型函数(GMF)的距离,仅利用海面风场反演伴随的最小残差即可构建CSCAT海冰识别模型。研究结果与欧洲气象卫星组织的海冰边缘线产品进行了比较,表明2021年9月南极和北极区域逐日的海冰覆盖面积估计标准差分别为1%和7%,与其他卫星散射计的海冰识别结果基本一致。这种新的海冰识别方法具有模型参数少、处理速度快、检测结果可靠的优点,对卫星地面系统的业务化处理具有重要的借鉴意义。 | 刘建强 刘思琦 林文明 郎姝燕 何宜军 | 2023 | 海洋学报2023，45，6： | 0 |
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