利用DenseUNet深度神经网络数之联河湖遥感大数据的研究

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        遥感能低成本地解决全局监测问题。基于高分二号卫星遥感数据，数之联为智慧治理中心构建了成都市河湖遥感大数据分析体系。其核心机理是洁净、黑臭、绿藻和高泥沙含量的水体，会因其中悬浮物、叶绿素-a、泥沙等物质的含量不同而呈现不同的光谱特征。我们利用人工智能技术自动解译遥感图像，准确再现污染水体的分布，实现成都水体污染情况的整体感知。同时通过融合物联网数据与网络理政数据，从宏观到微观，全方位掌握河湖量分布，水质污染情况及污染演变趋势。

　　(1)技术路线

　　首先，将多张遥感图分别切成36000个网格，分析各网格云层覆盖率，并以覆盖率最低的网格为基底融合成完整无云影像，消除云层影响。

　　然后，利用DenseUNet深度神经网络对上万个水体样本数据集进行学习，得到水体提取模型，将原始遥感数据输入水体提取模型中，能够快速准确的对水体进行大规模自动提取，并根据提取结果计算区域水网密度指数。

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　　最后，通过分析水中不同污染成分对光谱响应的差异性，构建指标反演模型，计算水体中叶绿素a、有色可溶解有机物(CDOM)、悬浮沉积物(SSSC)等物质含量，识别水体污染类型(黑河污染、水体富营养污染、泥沙污染)，评估不同种类污染程度，建立水质综合感官指数。

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　　(2)数字成效

　　河湖资源监测方面，目前系统已识别了1200余处水资源对象，总面积约为340km²。水污染监测方面，与全国城市黑臭水体整治信息平台公示的41处黑臭河渠治理名单相比，我们发现其中有37处明显改善。此外还发现了其他黑臭水体3处，需要进一步关注的高泥沙含量水体4处。

　　(3)应用方向

　　在未来，随着遥感和其他相关数据的不断积累，我们还将进一步探索水体违规侵占识别、水体面积演变趋势等内容，并形成可全国推广的示范性应用。


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posted @ 2021-07-01 09:59  GCloud_Blog  阅读(122)  评论(0)  编辑  收藏  举报