摘要
撞击坑是月球表面最重要的地质构造之一,通过对"嫦娥一号"CCD影像中撞击坑的边缘清晰度进行评价,可以进一步反演出月球表面的风化程度、地表起伏等地质信息。提出一种基于图像清晰度评价的边缘清晰度评价方法,从空域的梯度、频域的高频分量以及信息论三个方面,运用基于Sobel算子、小波变换和信息熵的算法对撞击坑的边缘清晰度予以评价。设计出一种适应于月球撞击坑特征的BP神经网络,组合三种评价算法的结果作为其输入,进而得到最终的清晰度等级。将最终结果加载到具有自主知识产权的数字月球平台上予以全月性的展示和进一步分析。
Impact crater is one of the most important geological structures on the surface of moon, By appraising the edge definition of impact craters in the CCD images of ＂CE-I＂, some geological information can be inverted such as the rate of decay and the undulation of topography. Considering the gradient in spatial domain, the high-frequency component in frequency domain and the theory of information, an algorithm derived from image definition appraisal is presented, aiming at appraising the edge definition of impact craters. Altogether three algorithms are realized, respectively based on Sobel algorithm, wavelet transform and entropy of information. A kind of BP neural network which is adapted to the feature of impact crater is designed and results from different algorithms are combined as the inputs to the network, thus the final rank of the edge definition is achieved. The final results are loaded into the Digital Platform for Moon（DPM） to be displayed and make further analv,~i~
出处
《计算机工程与应用》
CSCD
2013年第15期179-183,233,共6页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)(No.2010AA12202)
关键词
撞击坑
边缘清晰度
评价算法
SOBEL算子
小波变换
信息熵
反向传播(BP)神经网络
数字月球平台
impact crater
edge definition
appraisal algorithm
Sobel algorithm
wavelet transform
entropy of information
Back Propagation （BP） neural network
Digital Platform for Moon （DPM）
