Paper Info
Title
Nonlinear estimation of scene parameters from digital images using zero-hit-length statistics
Abstract
A zero-hit run-length probability model for image statistics is derived. The statistics are based on the lengths of runs of pixels that do not include any part of objects that define a scene model. The statistics are used to estimate the density and size of the discrete objects (modeled as disks) from images when the image pixel size is significant relative to the object size. Using different combinations of disk size, density, and image resolution (pixel size) in simulated images, parameter estimation may be used to investigate the essential invertibility of object size and density. Analysis of the relative errors and 95% confidence intervals indicates the accuracy and reliability of the estimates. An integrated parameter r, reveals relationships between errors and the combinations of the three basic parameters of object size, density, and pixel size. The method may be used to analyze real remotely sensed images if simplifying assumptions are relaxed to include the greater complexity found in real data
Year
DOI
Venue
1993
10.1109/36.225539
Geoscience and Remote Sensing, IEEE Transactions
Keywords
Field
DocType
image processing,remote sensing,statistical analysis,complexity,density,digital images,discrete objects,errors,image processing,image resolution,integrated parameter,nonlinear estimation,parameter estimation,pixels,probability model,reliability,remotely sensed images,scene parameters,simulated images,size,zero-hit-length statistics
Computer vision,Probability model,Nonlinear system,Image processing,Digital image,Artificial intelligence,Pixel,Estimation theory,Confidence interval,Statistics,Image resolution,Mathematics
Journal
Volume
Issue
ISSN
31
3
0196-2892
Citations 
PageRank 
References 
1
1.33
1
Authors
4
Name
Order
Citations
PageRank
Chen, R.111.33
David L. B. Jupp24411.12
C. E. Woodcock316641.71
alan h strahler416530.21