Enhancing remote sensing image retrieval using a triplet deep metric learning network

Rui Cao, Qian Zhang, Jiasong Zhu, Qing Li, Qingquan Li, Bozhi Liu, Guoping Qiu

Research output: Journal PublicationArticlepeer-review

60 Citations (Scopus)


With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval (RSIR) method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two public RSIR datasets and show that our method significantly outperforms state-of-the-art.

Original languageEnglish
Pages (from-to)740-751
Number of pages12
JournalInternational Journal of Remote Sensing
Issue number2
Publication statusPublished - 17 Jan 2020

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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