The grid DEM(digital elevation model) generation can be from any of a number of sources:for instance,analogue to digital conversion of contour maps followed by application of the TIN model,or direct elevation point mo...The grid DEM(digital elevation model) generation can be from any of a number of sources:for instance,analogue to digital conversion of contour maps followed by application of the TIN model,or direct elevation point modelling via digital photogrammetry applied to airborne images or satellite images.Currently,apart from the deployment of point-clouds from LiDAR data acquisition,the generally favoured approach refers to applications of digital photogrammetry.One of the most important steps in such deployment is the stereo matching process for conjugation point(pixel) establishment:very difficult in modelling any homogenous areas like water cover or forest canopied areas due to the lack of distinct spatial features.As a result,application of automated procedures is sure to generate erroneous elevation values.In this paper,we present and apply a method for improving the quality of stereo DEMs generated via utilization of an entropy texture filter.The filter was applied for extraction of homogenous areas before stereo matching so that a statistical texture filter could then be applied for removing anomalous evaluation values prior to interpolation and accuracy assessment via deployment of a spatial correlation technique.For exemplification,we used a stereo pair of ASTER 1B images.展开更多
This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effect...This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.展开更多
基金Supported by the Ministry of Human Resource Development (MHRD),India (for Distinguished Institute Fellow)
文摘The grid DEM(digital elevation model) generation can be from any of a number of sources:for instance,analogue to digital conversion of contour maps followed by application of the TIN model,or direct elevation point modelling via digital photogrammetry applied to airborne images or satellite images.Currently,apart from the deployment of point-clouds from LiDAR data acquisition,the generally favoured approach refers to applications of digital photogrammetry.One of the most important steps in such deployment is the stereo matching process for conjugation point(pixel) establishment:very difficult in modelling any homogenous areas like water cover or forest canopied areas due to the lack of distinct spatial features.As a result,application of automated procedures is sure to generate erroneous elevation values.In this paper,we present and apply a method for improving the quality of stereo DEMs generated via utilization of an entropy texture filter.The filter was applied for extraction of homogenous areas before stereo matching so that a statistical texture filter could then be applied for removing anomalous evaluation values prior to interpolation and accuracy assessment via deployment of a spatial correlation technique.For exemplification,we used a stereo pair of ASTER 1B images.
基金supported by the Student’s Platform for Innovation and Entrepreneurship Training Program(No.201510060022)
文摘This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.
