| 题名 | 遥感图像中的典型目标识别算法研究 |
| 作者 | 李伟 |
| 学位类别 | 工学博士 |
| 答辩日期 | 2011-11-18 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 中国科学院自动化研究所 |
| 导师 | 潘春洪 |
| 关键词 | 自动目标识别 感兴趣区域 视觉显著性 遥感图像 红外图像 合成孔径雷达 对称性检测 形状匹配 automatic target recognition region of interest visual saliency remote sensing image forward looking infrared image synthetic aperture radar symmetry detection shape matching |
| 其他题名 | Research on typical target recognition in remote sensing images |
| 学位专业 | 模式识别与智能系统 |
| 中文摘要 | 基于遥感图像的自动目标识别在军事和民用方面都有很重要的应用前景。然而，受到成像参数、目标姿态变化及识别任务需求复杂多变等因素的影响，自动目标识别仍是一个难点。本文以飞机、油罐和坦克这三类典型目标为研究对象，开展了目标感兴趣区域提取、目标识别、融合目标识别等方面的研究工作。论文的主要贡献体现在以下几个方面： 1. 提出了一种基于视觉显著性的目标感兴趣区域提取的方法。该方法模拟了人眼的注意机制，可以迅速对显著目标定位，且与目标的类型、姿态及图像类型无关。在视觉显著性的计算中我们引入了相位信息，把亮和暗的显著目标分成两个通道分别处理，提高了暗目标的检测率。在显著性区域分割中，通过空间竞争增强了目标的显著性，并采用了最大稳定极值区域检测的方法，提高了分割结果的鲁棒性。实验结果证明该方法在多种图像源上都有很好的稳定性。 2. 提出了一种机场典型目标的识别算法，该算法通过逐步深化的方法把视觉显著性计算等底层图像处理算法和目标的高层语义有机地结合在一起，一定程度上解决了目标识别中高层语义和底层算法之间的脱节。在飞机和油罐这两类典型目标的检测中，都采用了由粗到精的识别策略，先利用视觉显著性、对称性等变换不变的特征估计目标的精略位置与参数，再利用精确的形状匹配识别目标。该方法不仅提高了目标识别的精度，还大大减少了计算的复杂性。实验结果表明该算法具有很好的检测性能。 3. 提出了一种基于视觉显著性的融合目标识别算法框架，并给出了两种应用：融合可见光与红外图像的坦克目标分割，融合合成孔径雷达与可见光图像的油罐目标识别。把与目标识别直接相关的视觉显著性作为融合特征，有效地提高了目标的识别率，并降低了虚警率。融合框架可以针对不同的图像源和目标，融合能在不同的层次上并采用不同的策略实现，从而大大提高了融合识别的灵活性。 |
| 英文摘要 | Automatic target recognition in remote sensing images is of great importance in both military and civil application. Although object recognition technique has been developed for years, it remains a challenging problem due to background clutter, target pose variability, and partial occlusion. In this thesis, our work mainly focuses on recognition of several typical targets in remote sensing images, which include region of interest extraction, target detection, and fusion target recognition. The main contributions of the thesis are as following: 1. A new region of interest approach is proposed based on visual saliency. The method is built on human attention mechanism by which people quickly select regions of an image that contain salient objects without any prior information. We settle the visual saliency computation problem within the framework of scale-space theory, and the phase information provides a more promising approach to solve the problems caused by the non-negligible dark parts of target. We improve the saliency map by spatial competition algorithm, and a local adaptive thresholding segmentation algorithm is employed to segment saliency map into small regions, which has high accuracy and is insensitive to target type and size. Experimental results show that the proposed method is effective. 2. An efficient algorithm that recognizes typical objects in satellite airplane scenes is proposed. This algorithm systematically fuses low-level image saliency and high-level object semantics in a hierarchy, which somehow bridges the gap between the low- and high-level vision computations in the traditional satellite object recognizers. A coarse-to-fine strategy is used for both the airplane and oil tank recognitions. That is, we first rely on image saliency and object symmetry detection to roughly localize the positions of object targets, and then refine the localizations by shape matching. Extensive experiments show that this strategy not only significantly improves the recognition accuracy but also greatly reduces the computational complexity. 3. We propose a new fusion target recognition framework based on visual saliency. This algorithm has been used in tank detection by fusing forward looking infrared and optical images as well as oil tank detection by fusing Synthetic Aperture Radar and optical images. The proposed method offers a more flexible framework, it can be used in multi-source data and multi-class target, and can be applied at different ... |
| 语种 | 中文 |
| 其他标识符 | 200718014628047 |
| 内容类型 | 学位论文 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/6395]  |
| 专题 | 毕业生_博士学位论文 |
| 推荐引用方式 GB/T 7714 | 李伟. 遥感图像中的典型目标识别算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院. 2011. |
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