.png){kind=icon}
.png){kind=icon}
Return
Double random field models for remote sensing image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2003.09.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
By incorporating the local statistics of an image, a semi-causal non-stationary autoregressive random field can be applied to a non-stationary image for segmentation. Because this non-stationary random field can provide a better description of the image texture than the stationary one, an image can be better segmented. Besides low-order dependence among pixels in image for above-mentioned texture random field, the paper also introduces high-order dependence as a new classification feature to recognize the real object. Entropy rate that depicts the high-order dependence feature can also be estimated by using random field model. The proposed technique is applied to extract urban areas from a Landsat image. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
stochastic models
double random field
high-order feature
texture segmentation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W
Organization
No organization information available