Remote sensing scene classification method and device, terminal equipment and storage medium

Abstract

The invention belongs to the technical field of remote sensing images, and discloses a remote sensing scene classification method and device, terminal equipment and a storage medium. The method comprises the steps of obtaining a remote sensing scene image set, and inputting the remote sensing scene image set into a preset convolutional neural network model for feature extraction to obtain a top semantic feature set and a shallow appearance feature set; performing feature aggregation on the top semantic feature set through dense connection to obtain a first convolution feature; performing feature aggregation on the shallow appearance feature set to obtain a second convolution feature; performing feature compensation on the first convolution feature and the second convolution feature throughbidirectional gating connection to obtain a target convolution feature; and classifying the remote sensing scene images in the remote sensing scene image set according to the target convolution features. Characteristic aggregation is utilized, and shallow appearance characteristics and top semantic characteristics are complemented, so that shallow convolution characteristic information loss in aclassification characteristic aggregation stage is prevented.

Description

technical field [0001] The present invention relates to the technical field of remote sensing images, in particular to a remote sensing scene classification method, device, terminal equipment and storage medium. Background technique [0002] Since RS (Remote Sensing, Remote Sensing) scenes contain complex different types of land cover, classifying RS scenes is a difficult task, focusing on designing various human-engineered features using extensive engineering skills and domain expertise, For example: color, texture, shape, spatial, spectral information or combinations thereof. RS scene classification often has the situation that different scenes can be better distinguished by spectrum, shape or texture. At present, most works use fine-tuned pre-trained CNN (Convolutional Neural Networks, Convolutional Neural Networks) and aggregate convolutional features of different layers to classify complex remote sensing scenes. When aggregating the convolutional features of different...

AI Technical Summary

Problems solved by technology

[0003] The main purpose of the present invention is to provide a remote sensing scene classification method, device, terminal equipment and storage medium, aiming to solve the technical problem of how to reduce the loss of shallow convolution feature information when remote sensing scene classification

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