Abstract
Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned using only several tens of codes; this approach can improve the retrieval accuracy and decrease the time and storage requirements. To accomplish this goal, we first investigate the learning of a series of features with different dimensions using a few tens to thousands of codes via our improved CNN frameworks. Then, a Principal Component Analysis (PCA) is introduced to compress the high-dimensional remote sensing image feature codes learned by traditional CNNs. Comprehensive comparisons are conducted to evaluate the retrieval performance based on feature codes of different dimensions learned by the improved CNNs as well as the PCA compression. To further demonstrate the powerful ability of the low-dimensional feature representation learned by the improved CNN frameworks, a Feature Weighted Map (FWM), which can perform feature visualization and provides a better understanding of the nature of Deep Convolutional Neural Networks (DCNNs) frameworks, is explored. All the CNN models are trained from scratch using a large-scale and high-resolution remote sensing image archive, which will be published and made available to the public. The experimental results show that our method outperforms state-of-the-art CNN frameworks in terms of accuracy and storage.
Highlights
With the rapid development of Earth observation technology, remote imaging sensors with high spatial resolution have led to rapid increases in the volume of acquired remote sensing images.the effective management and retrieval of large scale remote sensing image databases represent considerable challenges that must be resolved
We show the Feature Weighted Map (FWM) based on the traditional Alexnet and VGG Deep Convolutional Neural Networks (DCNNs) frameworks for comparison
We propose learning Deep Compact Codes (DCC) for CB-HRRS-IR
Summary
With the rapid development of Earth observation technology, remote imaging sensors with high spatial resolution have led to rapid increases in the volume of acquired remote sensing images.the effective management and retrieval of large scale remote sensing image databases represent considerable challenges that must be resolved. Based on the extracted features, similarities between a query image and other images from the image database are calculated; the system returns the most similar images by ranking similarities Both modules play important roles in an image retrieval system. The length of the image features and the method of similarity measurement have a significant impact on the search efficiency, especially for enormous. Classical image features, such as spectral features [4,5], texture features [6,7,8], shape features [9,10] and morphological features [7] are the most common features used for remote sensing image representation. Recent studies have proposed methods that account for structure information in the image representations [14,15,16]
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