Semantic segmentation method based on multi-scale convolutional neural network

Abstract

The embodiment of the present invention discloses a semantic segmentation method based on a multi-scale convolutional neural network, which includes: acquiring high-resolution aerial images and intra-modal features in LiDAR point cloud data; performing modeling based on a multi-scale convolutional neural network; Feature extraction and classification between states; multi-scale segmentation method is used to extract the boundary of ground objects, eliminate the salt and pepper effect and optimize the classification results. Implementing the example of the present invention, the method of combining multi-scale CNN and multi-scale segmentation (MRS) post-processing is used for semantic segmentation of high-resolution aerial images and LiDAR point cloud data.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a semantic segmentation method based on a multi-scale convolutional neural network. Background technique [0002] Deep learning is a new field in machine learning research. Its purpose is to establish a neural network that simulates the human brain for analysis and learning. It imitates the mechanism of the human brain to interpret data, such as images, sounds and texts. This concept originates from the research of artificial neural network, which can be understood as the development of neural network. Its essence is to learn more useful features by building a machine learning model with multiple hidden layers and massive training data, thereby improving classification or prediction. accuracy. Commonly used deep learning models include convolutional neural networks (CNN), deep belief networks (DBNs), and so on. [0003] Convolutional neural network is a kind of artificial neu...

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Problems solved by technology

[0008] 3) Although the encoder-decoder CNN architecture can upsample the low-resolution features derived from the pooling layer to the input resolution, because the upsampling layer reconstructs the appearance of the object instead of the shape, resulting in the upsampled object boundary become blurred and irreversible

However, most of the existing LiDAR point cloud data and high-resolution aerial image fusion methods are loose fusion of intra-modal features, ignoring the statistical characteristics between modalities.

In addition, the convolutional neural network (CNN) uses tiles as input data, and the pixels at the edge of the tiles are prone to salt and pepper effects, resulting in uncertain labeling results

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