基于仿射不变离散哈希和条件随机场的遥感图像目标检测

Object Detection of Remote Sensing Image Based on AIDH and CRF

建立了一种结合仿射不变离散哈希(Affined-invariant discrete hashing,AIDH)和条件随机场(Confidential random field,CRF)的模型,实现遥感图像的目标检测。对遥感图像进行超像素分割,构建适用于CRF的以超像素块为顶点的无向图结构。以超像素块作为测试样本,使用AIDH学习方法作为CRF一元势函数,生成初始类别标签。采用Potts模型构建CRF的二元势函数进行标签的再学习,平滑目标邻域信息,解决目标检测中的漏判问题。最后,使用基于凸壳边界的方法生成最小外接目标框作为目标检测结果。实验表明,本文方法在目标检测的精度和效率上取得了较好的平衡。

By constructing a model combining affine-invariant discrete hashing(AIDH)and confidential random field(CRF),the object detection of remote sensing image is achieved.Firstly,the remote sensing image is reconstructed by superpixel segmentation,and the undirected graph structure with superpixel block as vertex is constructed for CRF.Then,the superpixel block is used as the test sample for AIDH learning which is used as CRF unary potential function to generate the initial category label.Then,the pairwise potential function of CRF is constructed by using Potts model for label re-learning,while the object neighborhood information is smoothed and the missing area of object detection is resolved.Finally,the convex hull boundary method is used for generating minimum external rectangular frame as object detection result.Experimental results demonstrate that the proposed method achieves the tradeoff of accuracy and efficiency for objection detection tasks.