A content-based image retrieval (CBIR) system works on the low-level visual features of a
user input query image, which makes it difficult for the users to formulate the query and also …

This letter introduces a novel unsupervised graph-theoretic approach in the framework of
region-based retrieval of remote sensing (RS) images. The proposed approach is …

Efficiently retrieving synthetic aperture radar (SAR) image is an important yet challenging
task in the remote sensing field. Due to the shortage of labeled SAR images for fine-tuning …

Learning effective feature representations and similarity measures are crucial to the retrieval
performance of a content-based image retrieval (CBIR) system. Despite extensive research …

With the rapid development of remote-sensing technology and the increasing number of
Earth observation satellites, the volume of image datasets is growing exponentially. The …

In this paper, we propose a novel framework for instance-level image retrieval. Recent
methods focus on fine-tuning the Convolutional Neural Network (CNN) via a Siamese …

This paper presents a novel graph-theoretic deep representation learning method in the
framework of multi-label remote sensing (RS) image retrieval problems. The proposed …

Deep learning techniques have become increasingly popular for classifying large-scale
image and video data. Remote sensing applications require robust search engines to …

Effective feature representations play an important role in remote sensing image analysis
tasks. With the rapid progress of deep learning techniques, deep features have been widely …

In content-based image retrieval, SIFT feature and the feature from deep convolutional
neural network (CNN) have demonstrated promising performance. To fully explore both …