兼顾面积属性与不确定性信息的样本点权重调整方法

样本点权重调整是遥感分类精度评价中样本点空间分配的关键环节。以北京市顺义区精度评价样本点为例,提出了一种兼顾面积属性与不确定性信息的样本点权重调整方法——模糊调整权重法,用于布设精度评价样本点。首先,构建用于表达不确定性信息的模糊中和指数及其权重,融合模糊中和指数权重和面积权重构建模糊调整权重,并计算各个分层的模糊调整权重结果,完成样本点特征空间分配;其次,设置不同梯度样本点集,结合平均最短距离最小化准则和空间模拟退火算法实现样本点地理空间优化布设;最后,构建权重调整效果评价指标,进行模糊调整权重效果评价,并与其他权重调整方法和未进行权重调整的布点方法进行对比分析。结果表明：顺义区不确定性大、中、小的层模糊调整权重分别为0.45、0.37、0.18,与面积权重相比,不确定性大的层权重显著增加、中层权重稍微增加、小层权重明显降低;5个不同数据集样本点权重调整的精度评价总体精度、相对精度、均方根误差和标准偏差结果分别为69.90%～73.48%、96.28%～99.82%、0.01和0.01;模糊调整权重布点方法评价效果优于面积权重、模糊中和指数权重、不确定性空间分层权重布点方法,以及空间...

Weight Adjustment Method of Sampling Sites Integrating Area Attribute and Uncertainty Information

Weight adjustment of sampling sites is a key aspect for spatial allocation of samples in the accuracy evaluation of remote sensing classification. Taking accuracy evaluation of sampling sites in Shunyi District of Beijing as an example, a weight adjustment method of sampling sites integrating area attribute and uncertainty information was proposed and named fuzzy adjustment weight method, which was used for sampling sites layout of accuracy assessment. Firstly, the fuzzy neutral index and its weight were constructed to stand for uncertainty information, and the fuzzy adjustment weight was constructed by fusing the fuzzy neutral index weight and area weight, and the fuzzy adjustment weight results of each stratum were calculated to achieve spatial allocation of samples in the feature space. Secondly, different gradient sample sets were drawn, and spatial-simulated annealing and the minimization of the mean of the shortest distances criterion were used to optimize sampling sites in geographical space. Finally, the indexes of weight adjustment evaluation were constructed to assess the effect of fuzzy adjustment weights. The comparative analysis was achieved between fuzzy adjustment weight and the other weight adjustment methods and the methods without weight adjustment. The results showed that the fuzzy adjustment weights of large, medium and small uncertainty strata in Shunyi District were 0.45, 0.37 and 0.18, respectively. Compared with the area weights of each stratum, the weights of large and medium uncertainty strata were increased significantly and slightly, respectively, and the weight of small uncertainty stratum was decreased significantly. The overall accuracy, relative accuracy, root mean square error and standard deviation of the accuracy evaluation results for weight adjustment of five different sample sets were 69.90%~73.48%, 96.28%~99.82%, 0.01 and 0.01, respectively. The evaluated effect of fuzzy weight adjustment method was better than the methods with area weight, fuzzy neutral index weight, uncertainty stratification weight, and the spatial even sampling and simple random sampling methods. The weight adjustment of sampling sites for the developed method was more accurate and reliable. The developed fuzzy adjustment weight method used for sampling sites layout of accuracy assessment can integrate the area attribute and uncertainty information, and avoid excessive weight adjustment, which was used to improve the rationality for spatial allocation of sampling sites in each stratum.