Summary of the invention
The object of the present invention is to provide it is a kind of can overcome drawbacks described above, using sampling camera photochrome or aerophotograph can know
Not Hua Po, the highway threat information recognition methods that threatens of the highways such as barrier lake.Its technical solution is using following steps:
1) training, test set are established:
(1) it takes pictures to highway, atural object sample point is acquired on the photo or aerophotograph taken, construct ground object sample collection:
Wherein, in formula (1) n be each subset in air route sum, S indicates total sample set, S1、S2、Si、SnRespectively
For the 1st, 2, i and n sample set;
(2) it selects representative region to establish sample set: selecting typical region for each sample set, and in representative region
Sample point acquisition is carried out, mathematical description is as follows:
Si={ xj|xj∈(z1, z2... zm), xj={ xjr, xjg, xjb} (2)
xjIndicate sample set SiJ-th of sampled point, m is the typical sampling area number selected, z1、z2、zmRespectively
1st, 2 and m typical sampling area, xjr、xjg、xjbRespectively indicate the picture of the corresponding red, green, blue wave band acquisition of j-th of sampled point
Element value;
(3) the tally set LB assignment of sample set S, mathematical description are as follows:
Wherein, LB is the corresponding tally set of sample set, LB1、LB2、LBnRespectively sample set S1, sample set S2, sample
Subset SnThe 1st, 2 and n sub-set of tags;
2) typical feature information extraction, comprising the following steps:
(1) standardization of sample set S data:
Zx in formula (4), (5) and (6)jr、zxjg、zxjbThe picture of red, green, blue wave band acquisition after respectively indicating standardization
Minimum value and max function are sought in element value, min () and max () expression;
(2) synthesis of red, green, blue wave band standardization pixel value collection and its tally set:
Sj=[zxjr, zxjg, zxjb] (7)
LB in formula (8)jr, LBjgAnd LBjbRespectively indicate zxjr、zxjgAnd zxjbCorresponding label value, bracket [] table
Show matrix, the comma in matrix is for segmentation between the column and the column;
(3) sample set S is divided for training set StrWith test set Ste, tally set LB is divided for training set LBtrAnd test set
LBte:
NStr≥NSTe,NLBtr≥NLBte, NStr+NSte=n, NLBtr+NLBte=n (11)
NS in formula (9)~(11)tr、NSteRespectively indicate the sample number of training set, the sample number of test set, NLBtr、
NLBteRespectively indicate the tally set sample number of training set and the tally set sample number of test set;
(4) threat identification model is constructed:
LModel={ Weight, error, ThrV } (12)
Weight=log10(1-error)/error) (13)
ThrV=min (sum (LBtr-LBtrf)) (14)
In formula (12), (13) and (14), Weight be forecast sample weight, error be forecast sample error and,
ThrV is the threshold value of label assignment, and sum () is summing function, LBtrfFor the training sample label value of prediction;
Classifier number range 1~20, according to the threat identification model of building, to step since first classifier
(1)~(3) training set, test set and its tally set obtained is trained, and the classification of training set is calculated according to formula (15)
Device error rate, until classifier error rate is not more than ThrV, deconditioning:
Etri=(NStr-Rtr)/NStr (15)
E in formula (15)triFor the error in classification rate that i-th of classifier of training set obtains, RtrFor the correct number of classification;
(5) test data set and its tally set are substituted into threat identification model, and computation model error rate:
Etei=(NSte-Rte)/NSte (16)
E in formula (16)teiFor the error in classification rate that i-th of classifier of test set obtains, RteFor the correct number of classification;
3) highway threat information identifies:
(1) the picture aerophotograph of information to be extracted is digitized;
(2) the picture aerophotograph after digitlization is standardized;
(3) standardization result of photo or aerophotograph is substituted into threat identification model formation (12)~(14), realizes and threatens letter
Breath identification.
Compared with prior art, the present invention advantage are as follows:
A. this method has the ability of the highways threat information identification such as processing landslide and barrier lake, for similar scene occurs
Emergency relief provides accident treatment template and guidance;Meanwhile by different photos or the pixel data of the red, green, blue color of aerophotograph
The threat identification of standardization building can be improved the recognition efficiency of threat, provide one kind effectively to solve the highway threat identification
Method;
B. position and the range of threat information can be quickly identified by aerophotograph or photochrome, this method is to remote sensing shadow
As the advantageous supplement in the case of data deficiencies, the efficiency of threat identification can be improved, have in emergency management and rescue decision Rapid Implementation
It plays an important role.
Specific embodiment
Technical solution of the present invention is described further below with reference to embodiment.Specific steps are as follows:
1) training, test set are established:
(1) photochrome comprising road is selected, constructs ground object sample collection S, sample set S includes road sample set
S1With non-rice habitats sample set S2, and
It (2) is sample set S1, sample set S2Respectively 5 typical region z of selection1~z5, and in sample set S1, sample
Subset S2Representative region in respectively acquire 100 sample points, xjr、xjg、xjbRespectively indicate j-th of sampled point it is corresponding it is red, green,
The pixel value of blue wave band acquisition:
x1r=[134 114 147 111 112 108 104 122 112 134 132 114 117 115 115 129
112 138 113 120 142 151 124 130 130 130 121 123 121 125 122 119 161 147 126
123 131 140 153 113 127 121 129 122 133 125 130 131 126 119 108 152 136 118
129 127 129 130 138 152 120 151 147 150 150 141 145 148 147 145 154 154 165
158 162 163 162 159 165 153 147 148 149 136 149 147 138 106 126 144 88 93 100
102 94 96 97 81 98 104 99 89 100 92 122 95 98 95 84 87 104 95 91 99 88 103 96
80 89 97 94 104 85 88 104 95 91 99 88 103 95 81 88 98 94 104 86 88]
x1g=[96 76 77 69 72 76 69 81 77 90 85 70 71 74 81 88 73 88 68 87 80
94 84 89 89 82 81 89 77 94 81 79 100 84 86 85 86 85 86 69 75 82 80 78 84 80
77 81 93 77 69 83 92 77 88 75 88 82 75 8681 81 126 131 129 119 125 126 130
127 134 133 148 143 141 142 137 137 146 131 125 131 129 117 126 126 117 92
108 116 78 81 90 86 82 86 87 70 84 90 89 78 86 82 103 83 82 84 84 79 92 89 81
85 77 91 86 74 77 82 84 92 79 76 92 89 81 85 77 91 87 74 78 82 84 93 79 76]
x1b=[87 73 67 70 64 77 67 75 71 81 77 67 73 72 79 84 68 81 65 78 69
87 82 85 85 82 79 80 74 92 75 77 81 79 84 84 83 78 78 69 77 75 83 75 80 77 69
80 86 78 64 76 89 73 82 77 84 82 66 70 76 71 107 114 108 106 100 103 104 103
109 112 122 122 114 115 115 114 131 108 102 105 105 103 110 109 100 92 104
105 77 83 89 86 84 87 86 76 84 89 90 86 86 81 99 83 79 82 85 77 92 89 82 85
83 95 87 74 79 79 83 92 79 78 92 89 82 85 83 95 88 75 79 79 84 92 77 78]
x2r=[85 83 80 78 79 56 49 76 56 51 73 65 67 67 61 48 65 57 66 54 65
23 67 66 59 45 60 49 37 57 57 63 69 58 53 65 50 62 43 62 75 73 66 64 64 64 75
52 59 55 60 56 41 49 72 45 70 65 58 53 43 30]
x2g=[72 68 68 66 68 52 44 68 57 50 70 61 59 57 58 43 57 51 58 47 62
23 57 59 56 39 59 43 36 51 47 64 63 53 49 64 49 56 41 59 64 63 58 64 64 66 61
48 55 46 55 50 40 45 61 42 71 62 51 52 41 30]
x2b=[66 63 68 66 66 51 38 66 49 45 63 58 56 56 53 39 54 51 56 39 57
23 55 53 49 39 54 45 32 51 46 59 63 49 48 60 45 56 44 54 60 61 56 62 62 65 60
45 52 39 52 50 35 42 59 37 65 57 52 48 44 28]
(3) to sample set S1Tally set LB1, sample set S2Tally set LB2Assignment, mathematical description are as follows:
LB1=1, LB2=2 (3)
2) typical feature information identifies, the specific steps are as follows:
(1) standardization of sample set S data:
Zx in formula (4), (5) and (6)jr、zxjg、zxjbThe picture of red, green, blue wave band acquisition after respectively indicating standardization
Minimum value and max function are sought in element value, min () and max () expression;
(2) synthesis of red, green, blue wave band standardization pixel value collection and its tally set:
Sj=[zxjr, zxjg, zxjb] (7)
LB in formula (8)jr、LBjgAnd LBjbRespectively indicate zxjr、zxjgAnd zxjbCorresponding label value, bracket [] table
Show matrix, the comma in matrix is for segmentation between the column and the column;
(3) sample set S is divided for training set StrWith test set Ste, tally set LB is divided for training set LBtrAnd test set
LBte, n value is 200, NStr、NSteRespectively indicate the sample number of training set, the sample number of test set, NLBtr、NLBteIt respectively indicates
The tally set sample number of training set and the tally set sample number of test set, are 100:
(4) threat identification model is constructed, classifier number 20 is set, training set, the test set that step (1)~(3) are obtained
And its tally set substitutes into threat identification model, and calculates 20 training set classifier error rates:
LModel={ Weight, error, ThrV } (12)
Weight=log10(1-error)/error) (13)
ThrV=min (sum (LBtr-LBtrf)) (14)
Etri=(100-Rtr)/100 (15)
(5) experiment scene of embodiment is 4: first experiment scene is the high-resolution remote sensing image of residential quarters,
Second environment is the landslide image picture downloaded on network, and third environment is similarly the high-resolution weir downloaded on network
Lake picture is filled in, the scene of the 4th rescue is the mud-rock flow photo downloaded on network.By the test of 4 experiment scene photochromes
Data set and its tally set substitute into threat identification model respectively, calculate its error rate:
Etei=(100-Rte)/100 (16)
3) highway threat information identifies:
(1) digitlization of the picture aerophotograph of information to be extracted is realized;
(2) digitized picture aerophotograph is standardized;
(3) standardization result of photo is substituted into threat identification model formation (12)~(14), realizes threat information identification.