Deep Hashing Learning For Visual And Semantic Retrieval Of Remote Sensing Images

Abstract

Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In general, existing retrieval methods can be regarded as visual-based retrieval approaches which search and return a set of similar images from a database to a given query image. Although retrieval methods have achieved great success, there is still a question that needs to be responded to: Can we obtain the accurate semantic labels of the returned similar images to further help analyzing and processing imagery? Inspired by the above question, in this paper, we redefine the image retrieval problem as visual and semantic retrieval of images. Specifically, we propose a novel deep hashing convolutional neural network (DHCNN) to simultaneously retrieve the similar images and classify their semantic labels in a unified framework. In more detail, a convolutional neural network (CNN) is used to extract high-dimensional

Stats

• citations63
• S2 citations—
• github stars0
• HF likes0
• heat score13.55
• arxiv keysong2019deep

Related papers

• Asymmetric Hash Code Learning For Remote Sensing Image Retrieval (2022)11.76
• Metric-learning Based Deep Hashing Network For Content Based Retrieval Of Remote Sensing Images (2019)13.93
• SSDH: Semi-supervised Deep Hashing For Large Scale Image Retrieval (2016)15.40
• Unsupervised Semantic Deep Hashing (2018)10.48
• Deep Hashing With Semantic Hash Centers For Image Retrieval (2025)2.26
• Deep Learning For Image Search And Retrieval In Large Remote Sensing Archives (2020)10.74
• Deep Unsupervised Contrastive Hashing For Large-scale Cross-modal Text-image Retrieval In Remote Sensing (2022)0.00
• Deep Semantic Multimodal Hashing Network For Scalable Image-text And Video-text Retrievals (2019)14.43