光学遥感图像多目标检测及识别算法设计与实现

针对目前光学遥感图像处理与分析多集中在单目标检测及识别领域的局限性,多目标检测及识别成为了一个非常值得关注的研究课题,提出了一种光学遥感图像多目标检测及识别算法.首先,采用自适应阈值算法对目标快速检测分割;然后,结合图像金字塔思想和基于尺度不变特征变换的特征包(BoF-SIFT)特征提出了一种分层的BoF-SIFT特征表示目标的全局特征和局部特征,详细地描述了目标的分布特性;最后,采用基于径向基核函数的支持向量机为弱分类器的AdaBoost算法,经过不断更新权重之后得到一个强分类器对待测试目标图像完成分类识别,识别率达到了93.52%.实验结果表明,所提算法对多类遥感图像目标的分割效果显著,特征选取恰当,识别方法快速有效.

Design and implementation of multi-target detection and recognition algorithm for optical remote sensing image

At present, the processing and analysis of optical remote sensing images is mostly concentrated on the detection and recognition of single target. The multi-target detection and recognition has become a very important research topic. A multi-target detection and recognition algorithm for optical remote sensing images was proposed. Firstly, the adaptive threshold algorithm was used for target detection and segmentation quickly. Then, a hierarchical BoF-SIFT (Bag of Feature-Scale Invariant Feature Transform) feature was constructed by effectively combing the image pyramid and BoF-SIFT feature to represent the global features and local features of the target and describe the distribution characteristics of the target in detail. Finally, the support vector machine based on radial basis function was used as weak classifier for AdaBoost algorithm. Then a strong classifier was obtained to complete classification and recognition of the target image after continuous updating weights. The recognition rate reached 93.52%. A large number of experimental results show that the proposed algorithm has a notable effect on the segmentation of multi-class target in remote sensing images. The feature is selected appropriately and the recognition method is fast and effective.