摘要
对多卫星传感器数据进行融合,首先要将多个传感器数据通过重采样算法重新投影到标准网格上。本文运用一种基于多边形切割算法的通量守恒重采样算法对图像数据进行重采样,并将该算法与3种常用的重采样算法(最邻近插值法、双线性插值法、三次卷积插值法)在信息保真方面的性能进行了比较。将所比较的重采样方法应用于两幅具有代表性的图像,其中一幅为人造图像,用于定性比较各种采样方法在图像缩放中的采样精度;另一幅为某机场卫星遥感图像,用于评价各种重采样方法在旋转图像方面采样的性能,并以定量参数(相关系数及光谱真实性)比较各种采样方法。结果表明,通量守恒重采样法对原始图像的信息保真效果最好,更适用于卫星遥感图像数据融合中的重采样。
For the merging of data from multiple missions, the original data from multiple missions should be re- projected in the standard grids by applying relevant resampling methods. The performance of the Flux-conserving Resarnpling method based on polygon clipping technique was compared with three commonly used methods （Nearest Neighbor Interpolation, Bilinear Interpolation, and Cubic Convolution Interpolation） in the information fidelity of an image data. The four methods were applied to two representative images. One is an artificial image, which is used to qualitatively compare resampling precision of resampling methods for image scaling; The other is a remote sensing image of an airport, which is used to compare the performance and quantitative parameters（Correlation Co- efficient and Spectral Fidelity） of these resampling methods for image rotation, The analysis results show that, Flux-conserving Resampling method has the best performance in preserving the original image information, and more suitable for resampling remote-sensing image data in the data merging.
出处
《中国海洋大学学报（自然科学版）》
CAS
CSCD
北大核心
2014年第6期103-108,共6页
Periodical of Ocean University of China
基金
国家自然科学基金项目(41276041
40876005)资助
关键词
通量守恒重采样法
最邻近插值法
双线性插值法
三次卷积插值法
flux-conserving resampling method
nearest neighbor interpolation
bilinear interpolation
cubic convolution interpolation
