Share Email Print

Proceedings Paper • new

Remote sensing image segmentation based on a modified pulse coupled neural network
Author(s): Sun Li; Nian Hua
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In order to adaptively adjust the model parameters and the global threshold for image segmentation, an improved pulse coupled neural network (PCNN) model based on human visual system (HVS) is proposed in this paper. Due to the property of HVS that human visual sensitivity to an image varies with different regions of the image where different regions correspond to different information rate area of the image, we analyze the characteristics of the improved model and its parameter optimization principle,and propose an improved segmentation algorithm. According to the gray scale of pixels, the algorithm adaptively realizes the division of the image information area. It not only preserves the excellent characteristics of PCNN for image segmentation, but also effectively preserves the gradation of image itself. The experiment results show that the algorithm proposed is efficient and has good segmentation effect, and has a wide application prospect in remote sensing image processing.

Paper Details

Date Published: 12 December 2018
PDF: 5 pages
Proc. SPIE 10846, Optical Sensing and Imaging Technologies and Applications, 108461S (12 December 2018); doi: 10.1117/12.2505168
Show Author Affiliations
Sun Li, Beijing Institute of Space Mechanics and Electricity (China)
Nian Hua, Beijing Institute of Space Mechanics and Electricity (China)

Published in SPIE Proceedings Vol. 10846:
Optical Sensing and Imaging Technologies and Applications
Mircea Guina; Haimei Gong; Jin Lu; Dong Liu, Editor(s)

© SPIE. Terms of Use
Back to Top