A remote sensing image multi-label retrieval method and system based on a full convolutional neural network

Abstract

The invention provides a remote sensing image multi-label retrieval method and system based on a full convolutional neural network. multi-label image retrieval is realized by considering multi-category information of remote sensing images, and the method comprises the following steps: inputting a retrieval image library, and dividing the retrieval image library into a training set and a verification set; Constructing a full convolutional neural network model FCN, and performing network training by using the training set; Performing multi-class label prediction on each image in the verificationset by using FCN to obtain a segmentation result; Carrying out up-sampling on each convolutional layer feature map; Extracting local features of each image in the verification set to obtain feature vectors for retrieval; And finally, carrying out coarse-to-fine two-step retrieval based on the extracted multi-scale features and the multi-label information. According to the method, the full convolutional neural network is used for learning multi-scale local features of the image, multi-label information hidden in the image is fully mined, and compared with an existing remote sensing image retrieval method based on a single label, the accuracy of image retrieval is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a remote sensing image multi-label retrieval method and system based on a fully convolutional neural network. Background technique [0002] Due to the rapid development of remote sensing technology at this stage, the amount of high-resolution remote sensing image data that can be obtained is increasing at an alarming rate. How to realize the efficient management and effective utilization of massive remote sensing data, and how to quickly and accurately mine the required information from the massive remote sensing data is a major problem in the field of remote sensing. As a method of information retrieval and mining, remote sensing image retrieval technology is an effective technical means to solve this problem. [0003] The current remote sensing image retrieval technology is mainly based on the image content. By extracting the low-level visual features or high-level sema...

AI Technical Summary

Problems solved by technology

For example, an image of a construction area often contains roads and trees in addition to buildings (buildings are the main category), but the existing remote sensing image retrieval methods based on manual and deep learning features usually only consider the main semantics contained in the image Content (single label), ignoring the multi-category information of the image

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