资源简介

摘要： 基于统计模型的降尺度方法被广泛用于热红外影像的尺度转换中，然而，大多数算法都会受到复杂地表环境的影响，例如地表覆盖、季节等。为了解决地表温度与光谱指数函数关系的不确定性，提出了一种新型的基于BP神经网络的地表温度降尺度方法。首先，在粗分辨率的情况下，训练得到一个以光谱指数为输入，原始温度为输出的BP神经网络。之后，输入高分辨率的光谱指数进而得到高分辨率的温度结果。实验通过设置多种光谱指数组合和BP网络隐藏层节点数而展开。结果评价时，以原始温度影像为参照，在城镇、植被和水体区域内，该方法的RMSE、R2、Bias及相对精度优于传统的分层线性回归降尺度方法。实测验证表明：该算法的RMSE和Bias分别达0.98 ℃、0.51 ℃，明显优于分层线性回归的结果(RMSE为2.9 ℃，Bias为1.7 ℃)，说明该方法具有较高的降尺度精度，这对于城市热环境的研究具有一定的应用价值。 关键词： 地表温度;  降尺度;  BP神经网络;  光谱指数;  Landsat 8 OLI     Abstract: Downscaling algorithms based on statistical models have been widely utilized to address the issue of coarse-resolution Land Surface Temperature (LST).However，most methods (e.g.，TsHARP algorithm) could be affected by land environment，including land cover，seasons.In this study，a Back Propagation (BP) neural network was introduced for LST downscaling in a specific area with complex land covers.The method comprises two steps.First，five reprehensive spectral indices were selected to training according to three typical land cover，including vegetation，building，and water.And the structure of network was trained using coarse-resolution spectral indices and LST.Second，high-resolution spectral indices were input to the network to get a high-resolution LST.A stratified linear regression downscaling with land-cover classification was conducted for comparative evaluation.The comparative results showed that in urban，vegetation，and water areas，the Root Mean Square Error (RMSE)，determination coefficient (R2)，and relative accuracy for the proposed approach (BP neural network) were better than those for stratified linear regression.Finally，the verification results show that RMSE and bias of the algorithm are 0.98 ℃ and 0.51 ℃，which is obviously better than the result of stratified linear regression (RMSE is 2.9 ℃ and Bias is 1.7 ℃).It shows that this method has a higher downscaling accuracy.And the approach is potential for producing high-resolution LST for the study on urban thermal environment.

相关资源

1. 水质遥感监测的关键要素叶绿素a的反演算法研究进展
2. 基于无人机遥感技术的森林参数获取研究进展
3. 基于无线传感器网络的森林生态系统观测试验平台构建
4. 基于无人机激光雷达的森林冠层高度分析
5. 多源时序国产卫星影像的森林火灾动态监测
6. 基于无人机遥感技术的台风灾害倒伏绿化树木检测

下载说明

·如果您发现该资源不能下载，请通知管理员.gissky@gmail.com

·为确保下载的资源能正常使用，请使用[WinRAR v3.8]或以上版本解压本站资源,缺省解压密码www.gissky.net ，如果是压缩文件为分卷多文件，请依次下载每一个文件，并按照顺序命名为1.rar,2.rar,3.rar...，然后鼠标右击1.rar解压.

·为了保证您快速的下载速度，我们推荐您使用[网际快车]等专业工具下载.

·站内提供的资源纯属学习交流之用,如侵犯您的版权请与我们联系.