Remote Sensing Image Content Retrieval Method Based on Semi-Supervised Deep Adversarial Self-Encoding Hashing Learning

Abstract

The invention discloses a remote sensing image content retrieval method for semi-supervised deep adversarial self-coding Hash learning. The method comprises the steps of establishing a remote sensingimage feature library, and selecting a plurality of samples as training samples; training an adversarial self-coding Hash learning model by using the training sample; performing Hash coding on the whole remote sensing image feature library by using an adversarial self-coding Hash coding model to obtain a Hash database; processing a query image input by a user, obtaining a feature vector corresponding to the query image through the same pre-training network, and performing Hash coding by using a confrontation self-coding Hash learning model to obtain a corresponding Hash code; and finally, calculating similar distances between the query image and all images in the image library, returning the images required by the user according to the distances from small to large, and finding the corresponding image in the remote sensing image library according to the index to complete image retrieval. According to the method, high retrieval precision can be kept under semi-supervised learning, Hashcoding is more efficient, smaller quantization loss is achieved, and the retrieval precision is further improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a remote sensing image content retrieval method based on semi-supervised deep confrontational self-encoding hash learning, which can be applied to large-scale remote sensing image retrieval. Background technique [0002] With the rapid development of remote sensing technology, the data volume of remote sensing images is increasing rapidly. The increasing amount of data brings convenience to people's life, but at the same time, how to effectively manage remote sensing data has become a challenge. Remote sensing image content retrieval refers to the ability to quickly retrieve interesting remote sensing images from massive databases, which is one of the effective methods to solve data management problems. How to realize efficient and fast image retrieval has important research significance. [0003] Hash retrieval refers to the extraction of ba...

AI Technical Summary

Problems solved by technology

Iterative quantization hashing (IQH) However, these methods require a lot of class label information when learning the hash code of the image, and the class label information requires a lot of manpower to label

However, using a small amount of class label information to learn hash coding, using traditional methods, will lose retrieval accuracy

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