Remote sensing image semantic segmentation method combining deep learning and random forest

Abstract

The invention relates to a remote sensing image semantic segmentation method combining deep learning and a random forest, and the method comprises the following steps: making a training data set of aresearch region, and employing samples and sample labels as the training data set; establishing a full convolutional neural network model, and training the model by using the sample and the sample label; extracting deep features of the research area by using a full convolutional neural network model; meanwhile, extracting shallow features of the GF-2 image of the research area; performing multi-feature combination on the deep features and the shallow features; performing semantic segmentation by adopting a random forest. According to the method, through the manufacturing of the data set, the used image wavebands are few, easy to obtain, high in universality and high in segmentation precision; the deep learning and random forests are combined, shallow features and deep features are innovatively fused in the method, the shallow features and the deep features are combined and complemented with each other, and the defects of a single method are overcome; the method has a good effect on remote sensing image semantic segmentation, the classification precision is effectively improved, and the method has a good effect on extraction of water bodies, vegetation and impermeable layers.

Description

technical field [0001] The invention relates to the technical field of remote sensing image classification, in particular to a remote sensing image semantic segmentation method combined with deep learning and random forest. Background technique [0002] Ground object information has always been extremely important information in remote sensing images. At present, the resolution of remote sensing images has been greatly improved. High-resolution remote sensing images have rich and fine ground feature information, and the details of ground features are clearer, which provides a good research basis for the extraction of ground feature information in remote sensing images. But at the same time, the structure of ground objects is more complicated, and the interference information is more difficult to deal with. [0003] Semantic segmentation of remote sensing images is the pixel-level classification of images, and is an important research direction in the application of remote s...

AI Technical Summary

Problems solved by technology

Although this invention can efficiently and quickly realize the pixel-level classification of various ground objects in high-resolution remote sensing images, and simplifies the complex process of traditional classification methods, the segmentation accuracy is low and the generality of the method is limited.

Images