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Study on Area Estimation of Small Water Using MODIS Data

机译:利用MODIS数据估算小水域面积的研究。

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In the low-resolution MODIS satellite remote sensing images, the edge of a small water body is often blurred due to the resolution limitation. The existence of mixed pixels is not conducive to water information extraction and observation. Two Dimension Scattered Points (2DSP)-Classification(C) NDVI method is introduced to area estimation of the small water body. This method first analyzes band 1 and band 2 of MODIS data through 2-dimensional visualization, second, selects sample point sets of surface categories distinguished by eyes to train BP neural network and then, discriminates and classifies pixels in the study region to get mixed pixels, finally estimates water area by using linear spectral mixing model. Above-mentioned method is applied to estimate area of the West Lake in Hangzhou. Estimate error is discussed in detail. The results shows that 2DSP-C NDVI method which has a better adaptability and a higher precision in quantitative calculation for small water area than other water information extraction methods, is applicable to real-time monitoring of small size water such as small lakes and reservoirs.
机译:在低分辨率的MODIS卫星遥感影像中,由于分辨率的限制,小水体的边缘经常模糊不清。混合像素的存在不利于水信息的提取和观测。将二维散点法(2DSP)-分类法(C)NDVI引入到小水体面积估计中。该方法首先通过二维可视化分析MODIS数据的波段1和波段2,其次,选择由眼睛区分的表面类别的采样点集来训练BP神经网络,然后对研究区域中的像素进行区分和分类以获得混合像素。 ,最后使用线性光谱混合模型估算水域面积。采用上述方法对杭州西湖面积进行了估算。估计误差将详细讨论。结果表明,与其他水信息提取方法相比,2DSP-C NDVI方法在小水域定量计算中具有更好的适应性和更高的精度,适用于小湖,水库等小水量的实时监测。

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