Remote Sensing Image Retrieval using Hybrid Visual Geometry Group Network with Relevance Feedback

Abstract

ABSTRACT The advancement in the field of remote sensing (RS) has offered a vast number of RS images with higher resolution. Nowadays, remote sensing image retrieval (RSIR) has become a challenging task for researchers due to the complicated contents and specific characteristics of RS images. Content-Based Image Retrieval (CBIR) methods create powerful tools for mining large RS image databases. Content-based RSIR aims to acquire the images with similar visual content based on a query given from a large-scale RS image library. Most of the previous works used pre-trained convolutional neural network (CNN) to form a scene illustration for the classification of RS scenes. In this work, an effective RSIR using hybrid VGGNet (Visual Geometry Group Network) CNN with red deer algorithm (RDA) is presented for the appropriate retrieval of RS images based on the query image. The proposed hybrid VGGNet CNN model integrates dimensionality reduction (DR), feature extraction (FE), loss function optimization, matching process and relevance feedback (RF) mechanism. The technique used to reduce the dimensionality is Principal Component Analysis (PCA). Next, feature extraction is processed using the combination of hybrid VGGNet CNN model, attention module and convolutional feature encoding module. The loss function is optimized using RDA, and the matching process is done by recursive density estimation (RDE). Finally, based on the feedback obtained from the user, the RF mechanism discards the images that are non-relevant and only relevant images are retrieved. The tool used for implementation is PYTHON platform. Hence, the extensive tests on University of California Merced (UCM) and Wuhan University Remote Sensing images with 19 class (WHU-RS19) databases reveal that the proposed model performs better than several state-of-the-art techniques.