High spatial resolution remote sensing images represent the surface of the earth in detail.As spatial resolution increases

spectral variability within the land cover units becomes complex in high spatial resolution remote sensing images

which makes traditional remote sensing image-processing methods on pixel basis such as ISODATA not suitable.Image segmentation that takes spatial information of image into account provides an alternative solution to this problem

and becomes a hot spot in the processing of high spatial resolution remote sensing image nowadays.Temporal Independent Pulse-Coupled Neural Network（TI-PCNN for short） is an improved PCNN

which is a useful biologically inspired image-processing algorithm. It has two properties including a neuron which has the ability to capture neighboring neurons in similar states and regions of neurons which are not connecting with each other

no matter in which states they are

have different pulsing time.These properties of the TI-PCNN ease difficulties of optimal parameters selection process commonly encountered in the usage of traditional PCNN

and make it a useful new tool in non-remote sensing 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 remote sensing image.By taking account of spatial and spectral characteristics of high spatial resolution remote sensing image

this paper studies the function of parameters in the TI-PCNN and proposes a segmentation method based on the TI-PCNN.A subset of aerial images with spatial resolution of 0.3m is used for experiment and analysis.Segmented result is compared with that of current TI-PCNN method and ISODATA.Result shows that our method can reduce variability within the land cover units to a large extent while maintaining geometric structure in the image.It provides a great potential in high spatial resolution remote sensing image segmentation.