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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A Landsat-8 OLI and Hyperion Images for Urban Land-Cover Classification
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Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A Landsat-8 OLI and Hyperion Images for Urban Land-Cover Classification

机译：基于双极化Sentinel-1ALandsat-8 OLI和Hyperion图像的多传感器卫星图像的多特征分类用于城市土地覆盖分类

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This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.
机译：本文着重评估使用从Sentinel-1A数据中提取的反向散射强度，纹理，相干性和颜色特征进行城市土地覆盖分类的能力和贡献，并比较不同的多传感器土地覆盖制图方法以提高分类准确性。结合Sentinel-1A数据，还获取了Landsat-8 OLI和Hyperion图像，以探索不同的多传感器城市土地覆盖制图方法提高分类精度的潜力。使用随机森林（RF）方法进行分类。结果表明，所有纹理特征组合的最佳窗口尺寸为9×9，并且每个单独的纹理特征的最佳窗口尺寸都不相同。对于四种不同的特征类型，纹理特征对分类的贡献最大，其次是相干和反向散射强度特征。颜色特征对城市土地覆盖分类的影响最小。仅使用纹理和相干特征的组合即可获得令人满意的分类结果，其总精度分别高达91.55％和kappa系数高达0.8935。在Sentinel-1A衍生的所有特征组合中，四个特征的组合具有最佳分类结果。多传感器城市土地覆盖图获得了较高的分类精度。与Sentinel-1A和Landsat-8 OLI图像的组合相比，Sentinel-1A和Hyperion数据的组合实现了更高的分类精度，总体精度高达99.12％，kappa系数高达0.9889。将Sentinel-1A数据添加到Hyperion图像后，总体准确性和kappa系数分别增加了4.01％和0.0519。

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