3种时空融合算法在洪水监测中的适用性研究
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石晨烈, 王旭红, 张萌, 刘状, 祝新明
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Analysis of the applicability of three remote sensing spatiotemporal fusion algorithms in flood monitoring
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Chenlie SHI, Xuhong WANG, Meng ZHANG, Zhuang LIU, Xinming ZHU
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表1 Gwydir研究区3种时空融合算法融合结果精度评估
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Tab.1 Accuracy assessment of synthesized Landsat-like images by STARFM,STRUM and FSDAF in Gwydir
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波段 | STARFM | STRUM | FSDAF | AD | RMSE | CC | SSIM | AD | RMSE | CC | SSIM | AD | RMSE | CC | SSIM | 蓝 | 0.011 | 0.016 | 0.597 | 0.566 | 0.011 | 0.015 | 0.615 | 0.611 | 0.010 | 0.014 | 0.667 | 0.653 | 绿 | 0.014 | 0.022 | 0.622 | 0.565 | 0.015 | 0.022 | 0.628 | 0.618 | 0.013 | 0.019 | 0.689 | 0.661 | 红 | 0.017 | 0.026 | 0.606 | 0.546 | 0.017 | 0.026 | 0.619 | 0.611 | 0.016 | 0.023 | 0.681 | 0.653 | 近红外 | 0.025 | 0.035 | 0.799 | 0.777 | 0.026 | 0.036 | 0.789 | 0.782 | 0.024 | 0.033 | 0.828 | 0.814 | 短波红外1 | 0.047 | 0.062 | 0.766 | 0.754 | 0.053 | 0.069 | 0.736 | 0.720 | 0.045 | 0.058 | 0.766 | 0.754 | 短波红外2 | 0.053 | 0.054 | 0.751 | 0.708 | 0.049 | 0.061 | 0.718 | 0.662 | 0.042 | 0.053 | 0.744 | 0.723 | 平均值 | 0.028 | 0.036 | 0.690 | 0.652 | 0.029 | 0.038 | 0.684 | 0.667 | 0.025 | 0.033 | 0.729 | 0.710 |
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