Change Detection Based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery

Abstract

In this letter, a change detection algorithm based on pulse-coupled neural networks (PCNN) and the normalized moment of inertia (NMI) feature is proposed for high spatial resolution (HSR) remote sensing imagery. To better analyze a large remote sensing image, the whole image is divided into blocks by the use of a deblocking mechanism. The PCNN model is utilized to obtain the initial binary image, and the NMI feature is calculated based on the binary image to detect the hot spot changed areas. Finally, the changed areas are processed by expectation–maximization to obtain the final change map. The experimental results using QuickBird and IKONOS images demonstrate that the proposed algorithm has the ability to provide better change detection results for HSR images than the traditional PCNN change detection algorithms.