DocumentCode
2670439
Title
High Spatial Resolution remote sensing Image segmentation using Temporal Independent Pulse Coupled Neural Network
Author
Liwei, Li ; Jianwen, Ma ; Xue, Chen ; Qi, Wen ; Xiaoyan, Xi
Author_Institution
CAS, Beijing
fYear
2007
fDate
23-28 July 2007
Firstpage
1915
Lastpage
1917
Abstract
Temporal independent pulse-coupled neuron network (TI-PCNN) has been developed and shows its usefulness on digital image segmentation. However, Due to its heavy computational cost and over-segmentation of objects within the range of low intensity, the original TI-PCNN method is ineffective at segmenting High Spatial Resolution remotely sensed Images (HSRI). By taking into account of spatial and spectral characteristics of HSRI, an improved method based on the TI-PCNN was developed and used to segment HSRI. Experiment was carried out on a subset of an aerial image. Result showed that the improved method largely overcomes the drawbacks of the original method and provided a promising approach for HSRI segmentation.
Keywords
geophysics computing; image segmentation; neural nets; remote sensing; computational cost; digital image segmentation; high spatial resolution remote sensing; spatial characteristics; spectral characteristics; temporal independent pulse-coupled neural network; Computational efficiency; Digital images; Earth; Image segmentation; Joining processes; Neural networks; Neurons; Pixel; Remote sensing; Spatial resolution; High spatial resolution remote sensing image; Neuron Network; Pulse-Coupled; Segmentation; Temporal-Independent;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location
Barcelona
Print_ISBN
978-1-4244-1211-2
Electronic_ISBN
978-1-4244-1212-9
Type
conf
DOI
10.1109/IGARSS.2007.4423200
Filename
4423200
Link To Document