一种自适应的方法修复MODIS坏像元波段反射率

发布时间：2018-02-08 19:51

本文关键词： 图像处理像元修复 MODIS 自适应筛选因子　出处：《安徽农业大学学报》2017年01期 　论文类型：期刊论文

【摘要】：遥感数据从时间尺度、空间尺度为人类生存的环境要素提取提供了有力的支持。遥感数据资源的重要性日益突出,数据积累周期越长其存在的潜在价值就越大,但是随着时间的推移,遥感数据也会随着传感器的衰退或损坏导致数据质量降低、无效数据等情况。针对该现象提出了一种自适应的多元拟合方法来修复光学遥感数据由于传感器损坏导致的无效观测;该方法通过考虑地物波谱反射特性实现坏像元修复,基于地物波谱在不同通道上的反射特性之间的关系,构建波谱自适应筛选因子来优化波段反射率修复模型,从而精确地修复坏像元的地表反射率。基于该方法,修复了Terra Moderate Resolution Imaging Spectroradiometer(MODIS)5波段数据在北京地区的无效观测(传感器坏像元);验证表明该方法精度统计指标RMSE为0.011,相比其他方法最好的结果提升大约8%,R~2达到0.82。整体上,该方法不但具有较好的精度,且具有更好的适用性。
[Abstract]:Remote sensing data from time scale and space scale provide powerful support for the extraction of environmental elements of human survival. The importance of remote sensing data resources is becoming increasingly prominent, the longer the data accumulation cycle, the greater its potential value. But as time goes by, remote sensing data can also degrade as sensors decay or damage. In this paper, an adaptive multivariate fitting method is proposed to repair the invalid observation of optical remote sensing data caused by sensor damage. Based on the relationship between the reflection characteristics of ground object spectrum on different channels, a spectral adaptive screening factor is constructed to optimize the wave band reflectivity restoration model, thus accurately repairing the surface reflectivity of the bad pixel. The invalid observation of Terra Moderate Resolution Imaging Spectroradiometer(MODIS)5 band data in Beijing area has been repaired. The verification shows that the statistical index RMSE of this method is 0.011, which is about 8% higher than the best result of other methods to 0.82%. This method not only has better precision, but also has better applicability.
【作者单位】：首都师范大学水资源安全北京实验室;首都师范大学资源环境与旅游学院;首都师范大学三维信息获取与应用教育部重点实验室;河南大学计算机与信息工程学院;安徽农业大学资源与环境学院;
【基金】：国家自然科学基金青年科学基金(41501380/D0106);国家自然科学基金重点项目(41130744/D0107) 中国博士后科学基金(2016M591209) 北京市博士后工作经费资助项目共同资助
【分类号】：TP79

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