Optical remote sensing image segmentation method based on multi-scale lightweight hole convolution

Abstract

The invention discloses an optical remote sensing image segmentation method based on multi-scale lightweight hole convolution, and mainly solves the problems of large storage space occupied by a network and poor image segmentation effect in the prior art. The method comprises the steps of acquiring optical remote sensing image data, and dividing a training sample set and a test sample set; constructing a multi-scale lightweight hole convolution network formed by cascading a feature extraction lower sampling sub-network, a bottom layer sub-network and an image recovery upper sampling sub-network; training the constructed multi-scale lightweight hole convolutional network by using the training sample set; and inputting the test sample set into the trained multi-scale lightweight hole convolutional network for testing to obtain a segmentation result of the optical remote sensing image. According to the method, the storage space occupied by the segmentation network is reduced, the segmentation precision of the optical remote sensing image is improved, and the method can be used for land planning management, vegetation resource investigation and environment monitoring.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an optical remote sensing image segmentation method, which can be used for land planning management, vegetation resource investigation and environmental monitoring. Background technique [0002] The segmentation of optical remote sensing images is one of the important research contents in the field of remote sensing. It refers to selectively locating the position and range of the object of interest in the image on the image taken by the optical remote sensing satellite that has been acquired. Different categories such as waters, buildings, forests, farmlands, grasslands, etc. [0003] At present, the optical remote sensing image segmentation methods include traditional image segmentation methods, image segmentation methods combined with specific tools, and image segmentation methods based on neural networks. in: [0004] Based on traditional image segmentation methods,...

AI Technical Summary

Problems solved by technology

However, in this type of method, because there is no efficient use of high-level features and low-level features, the image segmentation effect is not good.

Moreover, the network parameters of this type of method are complex and the amount of parameters is large, which makes the storage space occupied by the network large.

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