基于线性逻辑矢量模式的遥感图像目标检测

Remote sensing image target detection based on linearly logic vector pattern

针对局部线性模式(LLP)在遥感图像目标检测中存在维度较高及没有考虑相邻像素间联系的缺点,提出线性逻辑矢量模式特征.通过在2条互相垂直的方向上进行采样,选取出用于阈值比较的像素点,利用逻辑矢量变换的原理进行特征降维.通过阈值函数构建中心像素与采样点间的联系,提出基于中心的线性逻辑矢量模式特征(LLVP(C)),通过相邻点阈值比较模式联系相邻像素间的信息提出变型(LLVP(A)).为了糅合中心与相邻这2类特征信息,提出对LLVP(C)和LLVP(A)进行按位的逻辑融合得到新的LLVP.在遥感图像数据库上进行车辆、树木及建筑物的检测实验表明,LLVP优于其他的改进型LBP特征,表明应用LLVP特征再进行检测能够以较短的训练时长达到高精度及广适应性的双重标准.

Linear logic vector pattern features were proposed aiming at the shortcomings of local line pattern(LLP)in target detection of remote sensing images, such as high dimension and no connection between adjacent pixels. The pixels used for threshold comparison were selected by sampling in two mutually perpendicular directions, and the feature dimension was reduced by using the principle of logical vector transformation. A central based linear logic vector pattern(LLVP(C)) was proposed by constructing the relationship between the center pixel and the sampling point through the threshold function. An improved based linear logic vector pattern(LLVP(A)) was proposed by connecting the information between the adjacent pixels through the threshold comparison pattern of the adjacent points. A new LLVP was proposed based on the bit wise logical fusion of LLVP(C) and LLVP(A) in order to combine the center and adjacent feature information. The experiment of vehicle, tree and building detection on remote sensing image database shows that LLVP is obviously better than other improved LBP features. LLVP features can achieve high precision and wide adaptability with short training time.