Name
Title | ||
|---|---|---|
Multi-View Deep Representations With Cross-Dataset Transfer For Remote Sensing Image Retrieval And Classification |
Abstract | ||
|---|---|---|
Transfer learning is a challenging task in computer vision, due to the differences of data distribution. Although convolutional neural network (CNN) could learn different levels of image abstraction, the single-view features extracted from the final layer of a pre-trained or fine-tuned CNN may result in insufficient image description over different datasets. To address this issue, we focus on deep representations with multi-view analysis for remote sensing image (RSI) retrieval and classification tasks using five recently released large-scale datasets. First, cross-dataset transfer learning is presented by fine-tuning a pre-trained CNN on one dataset and testing the fine-tuned network on another one. Second, multi-view image representations are explored in terms of different activation vectors as well as CNNs. Finally, a multi-view fusion and a random projection (RP) strategy are proposed to improve the accuracies and computational cost of both RSI tasks, respectively. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-020-08712-0 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Convolutional neural network, multi-view, deep representation, image classification, image retrieval, remote sensing | Journal | 80 |
Issue | ISSN | Citations |
15 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Na Liu | 1 | 18 | 3.06 |
| Lihong Wan | 2 | 12 | 3.54 |
| Qiao Huang | 3 | 0 | 0.34 |
| Yunfeng Ji | 4 | 8 | 5.19 |