High Spatial Resolution remote sensing Image segmentation using Temporal Independent Pulse Coupled Neural Network

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 segmen...

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Bibliographic Details
Main Authors: Li Liwei, Ma Jianwen, Chen Xue, Wen Qi, Xi Xiaoyan
Format: Conference Proceeding
Language:English
Subjects:
Computational efficiency
Digital images
Earth
High spatial resolution remote sensing image
Image segmentation
Joining processes
Neural networks
Neuron Network
Neurons
Pixel
Pulse-Coupled
Remote sensing
Segmentation
Spatial resolution
Temporal-Independent
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Summary: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.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2007.4423200