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Content retrieval method for large-scale remote sensing images based on deep adversarial hashing learning

A remote sensing image, large-scale technology, applied in still image data retrieval, still image data indexing, still image data query, etc., to achieve the effect of improving retrieval accuracy, improving retrieval accuracy, and increasing retrieval efficiency

Active Publication Date: 2020-12-08
XIDIAN UNIV
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  • Content retrieval method for large-scale remote sensing images based on deep adversarial hashing learning
  • Content retrieval method for large-scale remote sensing images based on deep adversarial hashing learning
  • Content retrieval method for large-scale remote sensing images based on deep adversarial hashing learning

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[0055] The invention provides a large-scale remote sensing image content retrieval method based on deep adversarial hashing learning, and establishes a remote sensing image library{I 1 , I 2 ,...,I N}, the category corresponding to the image is {Y 1 ,Y 2 ,...,Y N}; Select 80% samples from each category to build a training sample library {I 1 , I 2 ,...,I l}; training depth against hashing learning model; using the trained depth against hashing learning model, for the entire library {I 1 , I 2 ,...,I N} for hash encoding to get the hash database of the image {B 1 ,B 2 ,...,B N}; For the query image I' input by the user, use the trained deep adversarial hash learning model to encode B'; calculate the similarity distance between the query image B' and the hash codes of all images in the hash database, and according to the distance Return the number of images required by the user in ascending order. The invention has the advantages of fast retrieval speed and high ret...

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Abstract

The invention discloses a large-scale remote sensing image content retrieval method based on deep adversarial Hash learning, and the method comprises the steps: firstly, building a remote sensing image library, and selecting a plurality of remote sensing images; using the constructed training sample to train a deep adversarial Hash learning model; performing Hash coding on the whole remote sensingimage library by using the trained adversarial Hash coding model to obtain a Hash database; after normalization processing is conducted on the query image input by the user, Hash coding is conductedthrough the trained confrontation Hash coding model, and Hash codes of the query image are obtained; calculating the similar matching distances between the Hash codes of the query images and all samples in a Hash database, returning the image indexes required by the user according to the matching distances from small to large, finding the corresponding images in the remote sensing image library according to the indexes, and completing the image retrieval. The method has the advantages of being high in retrieval precision, small in quantization loss and more efficient in Hash coding.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image content retrieval, and in particular relates to a large-scale remote sensing image content retrieval method based on deep adversarial 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 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 basic features (including...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/51G06F16/53
Inventor 马晶晶唐旭刘超焦李成
Owner XIDIAN UNIV
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