Low Dimensional Discriminative Representation of Fully Connected Layer Features Using Extended LargeVis Method for High-Resolution Remote Sensing Image Retrieval

Abstract

Recently, there have been rapid advances in high-resolution remote sensing image retrieval, which plays an important role in remote sensing data management and utilization. For content-based remote sensing image retrieval, low-dimensional, representative and discriminative features are essential to ensure good retrieval accuracy and speed. Dimensionality reduction is one of the important solutions to improve the quality of features in image retrieval, in which LargeVis is an effective algorithm specifically designed for Big Data visualization. Here, an extended LargeVis (E-LargeVis) dimensionality reduction method for high-resolution remote sensing image retrieval is proposed. This can realize the dimensionality reduction of single high-dimensional data by modeling the implicit mapping relationship between LargeVis high-dimensional data and low-dimensional data with support vector regression. An effective high-resolution remote sensing image retrieval method is proposed to obtain stronger representative and discriminative deep features. First, the fully connected layer features are extracted using a channel attention-based ResNet50 as a backbone network. Then, E-LargeVis is used to reduce the dimensionality of the fully connected features to obtain a low-dimensional discriminative representation. Finally, L2 distance is computed for similarity measurement to realize the retrieval of high-resolution remote sensing images. The experimental results on four high-resolution remote sensing image datasets, including UCM, RS19, RSSCN7, and AID, show that for various convolutional neural network architectures, the proposed E-LargeVis can effectively improve retrieval performance, far exceeding other dimensionality reduction methods.

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