Name
Abstract | ||
|---|---|---|
In the complex background, the contrast and signal-to-noise (SNR) ratio of the infrared (IR) small target are low. Therefore, the traditional IR small target detection algorithms are difficult to achieve good detection performance when the characteristics of small targets are sparse. To solve this problem, an IR small target detection network with generate label and feature mapping (GLFM)-net is proposed in this letter. First, in the GLFM-net model, a scale adaptive feature extraction network is proposed for the IR small target sparse features extraction, and then, the multilayer joint upsampling feature mapping network is proposed for small target feature mapping and background suppression. Based on this model, the feature mapping results of IR dim and small targets with the greatly suppressed background are obtained. Second, in model training, we designed a 2-D Gaussian label generation strategy for the problem of sample imbalance, which can achieve excellent detection performance by using small training samples. The experimental results show that the network can detect IR small targets with different sizes and low SNRs in various complex backgrounds and has good effectiveness and robustness compared with the existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2022.3140432 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Feature extraction, Clutter, Training, Object detection, Convolution, Data models, Kernel, Generate label and feature mapping (GLFM)-net, infrared (IR) small target, label strategy | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianlei Ma | 1 | 0 | 1.01 |
| Zhen Yang | 2 | 0 | 3.04 |
| Jiaqi Wang | 3 | 0 | 0.68 |
| Siyuan Sun | 4 | 0 | 0.68 |
| Xiangyang Ren | 5 | 0 | 0.34 |
| Usman Ahmad | 6 | 0 | 0.34 |