基于资源1号02D高光谱图像湿地水体分类方法对比——以白洋淀为例
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陈民, 彭栓, 王涛, 吴雪芳, 刘润璞, 陈玉烁, 方艳茹, 阳平坚
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A comparative study of water body classification of wetlands based on hyperspectral images from the ZY1-02D satellite: A case study of the Baiyangdian wetland
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CHEN Min, PENG Shuan, WANG Tao, WU Xuefang, LIU Runpu, CHEN Yushuo, FANG Yanru, YANG Pingjian
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表3 基于单点光谱的湿地分类精度
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Tab.3 Wetland classification accuracy based on different pixel-based methods
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模型 | 每类样本的分类准确率 | OA/% | mAP/% | F1 | R/% | 湖泊水面/% | 内陆滩涂/% | 村庄/% | 沼泽地/% | 水浇地/% | SVM | 97.51 | 59.66 | 99.27 | 46.75 | 92.60 | 83.90 | 89.73 | 0.82 | 89.30 | AdaBoost | 98.17 | 69.96 | 99.61 | 71.90 | 93.29 | 89.48 | 95.61 | 0.89 | 93.23 | RF | 97.72 | 61.96 | 98.43 | 57.54 | 91.51 | 85.33 | 92.67 | 0.84 | 90.06 | CNN1D | 97.51 | 84.76 | 99.56 | 90.11 | 96.10 | 94.54 | 98.13 | 0.95 | 95.75 | RNN | 95.80 | 87.08 | 99.13 | 85.68 | 96.09 | 94.03 | 97.74 | 0.94 | 94.95 | SSTN(Pixel) | 96.87 | 83.94 | 98.53 | 89.34 | 94.99 | 93.79 | 97.55 | 0.94 | 95.07 | ViT(Pixel) | 97.09 | 88.59 | 99.38 | 91.91 | 95.00 | 95.11 | 98.50 | 0.95 | 95.83 |
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