基于大数据分析的高分辨率可见光遥感卫星影像解译能力评估

Evaluation of high-resolution visible light remote sensing satellite image interpretation ability based on big data analysis

高分辨率可见光遥感卫星影像应用范围的不断扩大,对其解译能力提出了更高的要求。为了能够选取最佳的遥感卫星影像解译技术,需要对解译能力进行精准的评估与判断。本文结合海量影像数据,进行基于大数据分析的高分辨率可见光遥感卫星影像解译能力评估研究。深入分析遥感卫星影像解译能力影响因素,以此为基础,联合遥感卫星影像解译实际情况,选取适当的解译能力评估指标,应用大数据分析技术—模糊C均值(FCM)聚类算法对指标数据进行分类处理及其分析,基于熵权法计算解译能力评估指标的权重系数,结合当前解译能力评估指标数值,采用加权求和方法计算遥感卫星影像解译能力综合评估数值,从而实现了遥感卫星影像解译能力的有效评估。实验结果显示:应用该方法获得的遥感卫星影像解译能力评估时间最小值为16 s,解译能力评估结果与实际结果保持一致,充分证实了该方法解译能力评估性能较佳。

The continuous expansion of the application range of high-resolution visible light remote sensing satellite images has put forward higher requirements for their interpretation ability.In order to select the best remote sensing satellite image interpretation technology,it is necessary to accurately evaluate and judge the interpretation ability.Therefore,research on high-resolution visible light remote sensing satellite image interpretation ability evaluation based on big data analysis was conducted by using massive image data,and the influencing factors of remote sensing satellite image interpretation ability were analyzed.On this basis,combined with the actual situation of remote sensing satellite image interpretation,appropriate interpretation ability evaluation indicators were selected,and big data analysis technology was applied,namely fuzzy-c means(FCM)clustering algorithm,so as to classify and analyze the indicator data.The weight coefficient of interpretation ability evaluation indicators was calculated based on the entropy weight method.According to the value of interpretation ability evaluation indicators,the weighted sum method was used to calculate the comprehensive evaluation value of remote sensing satellite image interpretation ability,thus achieving effective evaluation of remote sensing satellite image interpretation ability.The experimental results show that the minimum evaluation time for remote sensing satellite image interpretation ability obtained by the proposed method is 16 seconds,and the evaluation results of interpretation ability are consistent with the actual results,fully confirming that the proposed method has better performance in interpretation ability evaluation.