High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective
AbstractBecause 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Xiao, Z.; Long, Y.; Li, D.; Wei, C.; Tang, G.; Liu, J. High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective. Remote Sens. 2017, 9, 725.
Xiao Z, Long Y, Li D, Wei C, Tang G, Liu J. High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective. Remote Sensing. 2017; 9(7):725.Chicago/Turabian Style
Xiao, Zhifeng; Long, Yang; Li, Deren; Wei, Chunshan; Tang, Gefu; Liu, Junyi. 2017. "High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective." Remote Sens. 9, no. 7: 725.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.