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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Automatic Change Detection in Very High Resolution Images With Pulse-Coupled Neural Networks
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Automatic Change Detection in Very High Resolution Images With Pulse-Coupled Neural Networks

机译：脉冲耦合神经网络在超高分辨率图像中自动检测变化

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摘要

A novel approach based on pulse-coupled neural networks (PCNNs) for image change detection is presented. PCNNs are based on the implementation of the mechanisms underlying the visual cortex of small mammals, and, with respect to more traditional NNs architectures, such as multilayer perceptron, own interesting advantages. In particular, they are unsupervised and context sensitive. This latter property may be particularly useful when very high resolution images are considered as, in this case, an object analysis might be more suitable than a pixel-based one. The qualitative and more quantitative results are reported. The performance of the algorithm has been evaluated on a pair of QuickBird images taken over the test area of Tor Vergata University, Rome.
机译：提出了一种基于脉冲耦合神经网络（PCNN）的图像变化检测新方法。 PCNN基于小哺乳动物视觉皮层基础机制的实现，并且相对于更传统的NNs体系结构（例如多层感知器）而言，它们具有有趣的优势。特别是，它们是无监督的且上下文相关。当考虑到非常高分辨率的图像时，后一种属性可能特别有用，因为在这种情况下，对象分析可能比基于像素的图像更适合。报告了定性和定量的结果。已经在罗马Tor Vergata大学的测试区域拍摄的一对QuickBird图像上评估了该算法的性能。

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