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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (1): 47-55    DOI: 10.3785/j.issn.1008-973X.2022.01.005
    
Remote sensing image target detection based on linearly logic vector pattern
Xue-yun CHEN(),Jin-han HUANG(),Zi-can HU,Sheng-cai CEN
School of Electrical Engineering, Guangxi University, Nanning 530000, China
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Abstract  

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.

Key words： remote sensing detection      logical vector transformation      binary mode      textural feature      logic fusion     
Received: 25 February 2021      Published: 05 January 2022
CLC:  TP 751  
Fund:  国家自然科学基金资助项目（62061002）
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Xue-yun CHEN
Jin-han HUANG
Zi-can HU
Sheng-cai CEN
Cite this article:

Xue-yun CHEN,Jin-han HUANG,Zi-can HU,Sheng-cai CEN. Remote sensing image target detection based on linearly logic vector pattern. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 47-55.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.01.005     OR     https://www.zjujournals.com/eng/Y2022/V56/I1/47


基于线性逻辑矢量模式的遥感图像目标检测

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.


关键词： 遥感检测,  逻辑矢量变换,  二值模式,  纹理特征,  逻辑融合 
Fig.1 Principle of transformation of different logic block
φ1函数 逻辑类型 φ2函数 逻辑类型
φ1_or 或 φ2_or 或
φ1_and 与 φ2_and 与
φ1_nor 或非 φ2_nor 或非
φ1_nad 与非 φ2_nad 与非
Tab.1 Logical types corresponding to transformation function
Fig.2 Sampling principle of LLVP(C)
Fig.3 Sampling principle of LLVP(A)
Fig.4 Principle of logic fusion
Fig.5 Part of positive and negative samples of remote sensing vehicles and remote sensing trees
模式 m 模式 m
LBP 5 376 LVP 4368
CLBP 5 376 LLVP(C) 2688
ULBP 1239 LLVP 2688
LLP 2478 ? ?
Tab.2 Dimensions of various features
φ1函数 特征 m 遥感车辆 遥感树木
A/% F1 A/% F1
φ1_or LLVP(C) 672 90.84 0.921 89.31 0.918
φ1_and LLVP(C) 672 91.43 0.928 88.97 0.902
φ1_nor LLVP(C) 672 89.77 0.915 87.51 0.881
φ1_nad LLVP(C) 672 90.05 0.908 88.43 0.894
φ1_or LLVP(A) 672 86.22 0.905 86.71 0.885
φ1_and LLVP(A) 672 87.64 0.911 85.97 0.871
φ1_nor LLVP(A) 672 86.14 0.889 86.36 0.879
φ1_nad LLVP(A) 672 86.17 0.896 86.98 0.884
φ2_or LLVP(C) 2 688 92.31 0.924 91.77 0.914
φ2_and LLVP(C) 2 688 91.91 0.926 90.12 0.907
φ2_or LLVP(A) 2 688 86.42 0.871 87.22 0.861
φ2_and LLVP(A) 2 688 88.21 0.881 87.58 0.872
Tab.3 Test results of LLVP(C) and LLVP(A) in BP network with logic transformation of different φ functions
φ函数融合的LLVP m 遥感车辆 遥感树木
A/% F1 A/% F1
LLVP1, [β_or]
LLVP1, [β_and]
LLVP1, [β_nor]
LLVP1, [β_nad] 		672
672
672
672 		94.03
94.96
93.47
93.88 		0.948
0.957
0.945
0.951 		92.43
92.44
90.51
91.89 		0.938
0.943
0.914
0.925
Tab.4 Test results of LLVP1 with different β functions in BP network
φ函数融合的LLVP m 遥感车辆 遥感树木
A/% F1 A/% F1
LLVP2, [β_or]
LLVP2, [β_and]
LLVP2, [β_nor]
LLVP2, [β_nad] 		2688
2688
2688
2688 		95.54
95.81
93.77
95.62 		0.961
0.967
0.943
0.952 		94.79
94.71
93.11
92.97 		0.955
0.945
0.939
0.945
Tab.5 Test results of LLVP2 with different β functions in BP network
算法 m ttr/h 遥感车辆 遥感树木
A/% F1 A/% F1
LBP
ULBP
LLP
LVP
YOLOv3
Faster R-CNN
LLVP(本文方法)		 5376
1239
2478
4368
?
?
2688		 0.39
0.12
0.2
0.36
18
22
0.23		 88.54
89.98
91.17
93.11
96.18
96.83
95.81		 0.891
0.901
0.923
0.937
?
?
0.967		 87.87
88.91
90.84
91.78
95.77
96.12
94.79		 0.881
0.887
0.914
0.927
?
?
0.955
Tab.6 Test results of various texture features trained by BP neural network and test results of YOLOv3, Faster R-CNN
Fig.6 P-R curves on remote sensing database
Fig.7 Detection effect pictures on remote sensing vehicles
Fig.8 Detection effect pictures on remote sensing trees
Fig.9 Detection effect pictures on remote sensing buildings
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