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Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution

机译:脉冲耦合神经网络,可在非常高的空间分辨率下自动检测城市变化

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In this paper, a novel unsupervised approach based on Pulse-Coupled Neural Networks (PCNNs) for image change detection is discussed. PCNNs are based on the implementation of the mechanisms underlying the visual cortex of small mammals and with respect to more traditional neural networks architectures own interesting advantages. In particular, they are unsupervised and context sensitive. The performance of the algorithm has been evaluated on very high spatial resolution Quick-Bird and WorldView-1 images. Qualitative and more quantitative results are discussed.
机译:本文讨论了一种基于脉冲耦合神经网络(PCNN)的无监督新型图像变化检测方法。 PCNN基于小哺乳动物的视觉皮层基础机制的实现,并且相对于更传统的神经网络架构,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。该算法的性能已经在非常高分辨率的Quick-Bird和WorldView-1图像上进行了评估。定性和更定量的结果进行了讨论。

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