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High Spatial Resolution Remote Sensing Image Segmentation Using Temporal Independent Pulse-Coupled Neural Network

机译:使用时间独立脉冲耦合神经网络的高空间分辨率遥感图像分割

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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.
机译:已经开发了临时独立的脉冲耦合神经元网络(TI-PCNN)并显示其对数字图像分割的有用性。然而,由于其在低强度范围内的物体的重量计算成本和过分分割,原始TI-PCNN方法在分割高空间分辨率(HSRI)方面是无效的。通过考虑到HSRI的空间和光谱特性,开发了基于TI-PCNN的改进方法并用于分段HSRI。在空中图像的子集上进行实验。结果表明,改进的方法在很大程度上克服了原始方法的缺点,并为HSRI分割提供了有希望的方法。

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