Elitist Chemical Reaction Optimization for Contour-Based Target Recognition in Aerial Images
Elitist Chemical Reaction Optimization for Contour-Based Target Recognition in Aerial Images
复制标题
航空图像中基于轮廓的目标识别的精英化学反应优化
DOI：
10.1109/tgrs.2014.2365749
复制
发表时间：
2015-05-01
影响因子：
8.2
通讯作者：
Gan, Lu
中科院分区：
文献类型：
--
作者：
Duan, Haibin;Gan, Lu
航空图像目标识别是遥感应用中的一个重要研究课题。许多基于特征的识别方法已经被引入到目标识别中。然而，考虑到卫星图像提供的大量数据，这些方法有其局限性。本文研究了几种基于轮廓匹配的航空图像目标识别技术。该方法采用轮廓线分组策略检测轮廓线，并用边缘势函数描述轮廓线，为具有相似曲线的边缘提供了一个吸引场。在这个意义上，目标识别可以被描述为一个优化问题。针对目标匹配问题，提出了一种改进的化学反应优化(CRO)算法。实验结果表明，与现有的进化算法相比，该算法具有较强的鲁棒性和较高的效率，这些进化算法包括原始CRO算法、基于捕食者-被捕食生物地理学的优化算法、改进的脑风暴优化算法、人工蜂群算法、量子行为粒子群算法、自适应差分进化算法和种子型遗传算法。此外，还介绍了几个关于遥感的案例研究。结果表明，该方法能够提高航空图像中目标识别的应用能力。
Target recognition for aerial images is an important research issue in remote sensing applications. Many feature-based recognition methods have been introduced for target recognition. Nevertheless, these methods have their limitations when considering the large amount of data provided by satellite imagery. In this paper, we explore several techniques for target recognition in aerial images with a contour matching approach. Contours in our approach are detected by a contour grouping strategy and described by edge potential function, which provides an attraction field for edges with similar curves. In this sense, target recognition can be formulated as an optimization problem. An improved chemical reaction optimization (CRO) algorithm is proposed in this paper to deal with the target matching problem. Experimental results demonstrate the robustness and high efficiency of our approach over the state-of-the-art evolutionary algorithms, which include the original CRO, predator-prey biogeography-based optimization, an improved version of brain storm optimization, artificial bee colony, quantum-behaved particle swarm optimization, a self-adaptive differential evolution algorithm, and stud genetic algorithm. In addition, several case studies regarding remote sensing are also presented. The results show that the proposed method is capable of improving the application ability of recognizing target in aerial images.