CN107066995A - A kind of remote sensing images Bridges Detection based on convolutional neural networks - Google Patents

A kind of remote sensing images Bridges Detection based on convolutional neural networks Download PDF

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CN107066995A
CN107066995A CN201710380211.6A CN201710380211A CN107066995A CN 107066995 A CN107066995 A CN 107066995A CN 201710380211 A CN201710380211 A CN 201710380211A CN 107066995 A CN107066995 A CN 107066995A
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sensing images
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刘兵
周勇
郑成浩
王重秋
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China University of Mining and Technology CUMT
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Abstract

The invention discloses a kind of remote sensing images Bridges Detection based on convolutional neural networks, for data volume and picture size all larger remote sensing images, detection efficiency is carried out to wherein Bridge position using conventional method low, of long duration.The present invention has initially set up convolutional neural networks model, and the bridge image that interception size size is w*h in remote sensing images initializes parameters in convolutional neural networks model, training sample is input in model and is trained as training sample.The remote sensing images to be detected window of w*h sizes is scanned according to step-length l in detection process, candidate window is drawn and marks good position information, finally candidate window is put into after model and exports remote sensing images Bridge position to be detected, realize detection.Feature extraction of the present invention without carrying out bridge picture in advance, simplifies detecting step, and the detection speed of remote sensing images is greatly accelerated while high detection rate is kept.

Description

A kind of remote sensing images Bridges Detection based on convolutional neural networks
Technical field
The present invention is applied to field of image recognition, is detected mainly for the bridge in remote sensing images, is a kind of base In the remote sensing images Bridges Detection of convolutional neural networks.
Background technology
Remote sensing image processing includes acquisition, denoising, enhancing, recovery, compression, segmentation, expression and description, the mesh of remote sensing images Mark detection etc..Wherein, target detection as remote sensing image processing a pith, in military field and civil area all Have great importance.In military field, it is necessary to carry out military surveillance to enemy and one's own side is monitored.By to defending The remote sensing images that star, aviation or aerospace craft are obtained carry out target identification, can understand landform, the dress in captured area The information such as standby, troop movements' situation.The Remote Sensing Target detection of early stage, which is used, manually to be carried out, but is due to usual acquisition Remote sensing image data amount it is very big, if carrying out interpretation using artificial, need repeated work, waste time and energy, and in real time Property is poor.Modern high-tech war, battlefield situation is fast changing, if image processing speed is too slow, it is impossible to obtain in time Key message is taken, causes to bungle the chance of winning a battle, one's own side is sustained a great loss.Therefore, remote sensing is carried out using fast automatic identification technology Image Automatic Target detection is extremely important to modern war.In addition to military important value, Remote Sensing Target detection The civil areas such as the condition of a disaster assessment of foundation and renewal, natural calamity in other side such as urban planning, geographical data bank also have And be widely applied.As the concepts such as global positioning system, GIS-Geographic Information System, digital earth system are proposed in succession, also get over More to need to carry out the target in remote sensing images accurate detection positioning.In addition, Remote Sensing Target detection is in accurate Drawing Also become to compel to be essential in the damage condition detection that two-dimensionally or three-dimensionally figure, natural calamity are caused and the change of target detection in city Will.
At present, the Bridge detection for remote sensing images, which is mainly used, extracts candidate region using conspicuousness method and carries Feature is taken, feature is carried out using grader to judge to obtain testing result.Patent No. CN200810232213.1 remote sensing figure As Bridge detection is to be trained modeling by watershed feature, remote sensing images Watershed segmentation is carried out with this, for splitting Result need during bridge machinery, detection bridge for the different template of different Bridge Designs, then extract Feature finally completes bridge machinery.
Based on the studies above present situation, the target of remote sensing images is primarily present following two problems：First, after pretreatment, The extraction of the artificial default specific features such as shape, length-width ratio or area of connected region is often carried out to sample image, so not It can guarantee that and extract effective or important feature, artificial experience influence is too big, and practical application effect is not good；Second, in order to not The minutia of image is lost, the process of artificial default feature extraction is also ignored sometimes, directly makees all pixels in image It is characterized, then these features is so done too cumbersome, can bring substantial amounts of superfluous as classifier training and the Back ground Information of classification Remaining information so that detection efficiency is reduced.
The content of the invention
Goal of the invention：It is an object of the invention to the advantage using convolutional neural networks in terms of image procossing, it is proposed that A kind of detection method that remote sensing images Bridge image is solved using convolutional neural networks.It the method overcome conventional method effect The low shortcoming of rate, by the feature in convolutional neural networks automatic mining image, finally realizes the detection of bridge picture.
Technical scheme：
A kind of remote sensing images Bridges Detection based on convolutional neural networks, including step：
S1：Training sample is gathered and pretreatment；
S1-1：The remote sensing images for including bridge area are chosen, manual interception size size is w*h sizes on remote sensing images Bridge picture；
S1-2：The region of bridge is not included on remote sensing images, interception size size is w*h picture, is used as detector Negative sample be trained；
S1-3：The positive negative sample obtained in selecting step S1-1, S1-2, on the premise of picture w*h sizes are kept, Align negative sample picture and carry out flip horizontal, change of scale, translation transformation, rotation transformation and whitening operation；
S2：Convolutional neural networks training pattern is set up, detector is obtained；
S2-1：Convolutional neural networks model is set up, and the parameters in convolutional neural networks model are initialized；
S2-2：The positive negative sample that step S1-1, S1-2 is obtained is put into the convolutional neural networks model that S2-1 is obtained, and carries out Repetitive exercise；
S3：Detect the pretreatment of sample：
Remote sensing image to be detected is chosen, is scanned, is laterally swept since the upper left corner of remote sensing image by w*h size windows Step-length is retouched for w/2, when the low order end of scanning to picture to be detected, a line is moved down according to longitudinal scanning step-length h/2, then from Far Left starts the step scan according to horizontal w/2, and a completely remote sensing image is scanned successively；Record what each step scanning was all obtained The position coordinates in the candidate window upper left corner, is used as the positional information of candidate's picture；
S4：Detection sample input detector obtains result；
S4-1：The candidate window that step S3 is obtained trains the input of obtained detector as step S2, to all Candidate window is detected, records the candidate's picture for being judged as including bridge by detector, and preserve these candidate windows；
S4-2：The positional information that the candidate window of preservation is included is extracted, then the basis on picture to be detected The positional information of candidate window marks the image-region representated by candidate window, is finally completed to remote sensing images Bridge position Detection work.
The step S1-1 should choose the obvious picture of bridge feature when bridge picture is intercepted, while also Interception includes bridge, but feature is not obvious, is blocked or more fuzzy bridge picture.
The convolutional neural networks model that the step S2-1 is set up includes input layer, convolutional layer, pond layer, convolutional layer, pond Change layer, full articulamentum and output layer；
1) input layers are, as input, to be input to positive negative sample in convolutional neural networks model；
2) the feature extractions first stage：The convolution kernel size of convolutional layer is 5*5, inputs 3 passages, exports 64 passages, is moved Dynamic step-length is 1；Pond layer is carried out by the way of maximum pond, and window size is 3*3, and step-length is 2, then by obtained feature Figure is normalized；
3) enters feature extraction second stage：The convolution kernel size of convolutional layer remains 5*5, inputs 64 passages, output 64 Passage, step-length is 1, then pond will be carried out after the characteristic pattern normalization operation after convolution, pond mode, which remains unchanged, takes maximum Chi Hua, window size is 3*3, and step-length is 2；
4) pond result is finally put into full articulamentum by, is finally exported.
Right value update in the convolutional neural networks model that the step S2-1 is set up is carried out using BP back propagations； The method selection gradient descent method of every layer of renewal weights；The Learning Rate learning rates of the gradient descent method are arranged on Between 0.003-0.004.
The last output for the convolutional neural networks model that the step S2-1 is set up uses Softmax as two graders, Softmax is returned in two steps：The first step is in order to obtain the evidence that a given picture belongs to some optional network specific digit class, to picture picture Plain value is weighted summation；If there is this pixel very strong evidence to illustrate that this pictures is not belonging to such, then corresponding Weights are negative, if this opposite pixel possesses favourable evidence and supports this pictures to belong to this class, then weights are just Number；I.e.：
evidenceiRepresent that given picture belongs to the evidence of i classes；Wherein wiRepresent weight, biRepresent the biasing of numeral i classes Amount, the pixel index that j represents given picture x is summed for pixel；Then these evidences can be converted into Softmax functions Probability y：
Y=softmax (evidence)
Wherein, Softmax is an excitation function, therefore, gives a pictures, it is for each digital goodness of fit One probable value is converted into by Softmax functions；Softmax functions are defined as：
Softmax (x)=normalize (exp (x))
Deploy the minor on the right of equation, obtain：
After the result of a probability distribution is obtained using Softmax graders, result is compared with final label It is right, and a threshold value T is determined by comparing, the threshold value is represented when the probable value in Softmax training results is more than T, then Judge to include bridge in input picture；If the probable value in training result is less than T, that judges not including bridge in input picture Beam.
During repetitive exercise in the step S2-2, the strategy of circuit training is taken；Every time from all samples pictures In randomly select a number of picture and be trained, the batch_size sizes of selection are 128, then randomly select same number Other samples of amount are trained, in constantly cyclic process, gradually update the weights in convolutional neural networks model.
Beneficial effect：Feature extraction of the present invention without carrying out bridge picture in advance, simplifies detecting step, is keeping high The detection speed of remote sensing images is greatly accelerated while verification and measurement ratio.
Brief description of the drawings
Fig. 1 is the inventive method flow chart.
Fig. 2 is convolutional neural networks structure chart.
Embodiment
The present invention is further described below in conjunction with the accompanying drawings.
The present invention is a kind of remote sensing images Bridges Detection based on convolutional neural networks, mainly include the training stage and Detection-phase, the described training stage mainly includes the following steps that：
S1:Training sample is gathered and pretreatment；
S2:Convolutional neural networks training pattern is set up, detector is obtained.
Described detection-phase is mainly included the following steps that：
S3:Detect the pretreatment of sample；
S4:Detection sample input detector obtains result.
Further, described step S1 includes following sub-step：
S1-1:A part of remote sensing images are chosen first, and manual interception size size is the bridge of w*h sizes on remote sensing images Beam picture, it is necessary to choose the obvious picture of bridge feature when bridge picture is intercepted, meanwhile, it should also intercept some bags Containing bridge, but feature is not obvious, is blocked or more fuzzy bridge picture, can so ensure in training positive sample Afterwards, detector also has certain detectability to the unconspicuous bridge picture of feature.
S1-2:The region of bridge is not included on remote sensing images, also interception size size is w*h picture, these pictures It is trained as the negative sample of detector.
S1-3：The positive negative sample obtained in selecting step S1-1, S1-2, on the premise of picture w*h sizes are kept, Align negative sample picture and carry out flip horizontal, change of scale, translation transformation, rotation transformation and whitening operation, so do further The quantity of training sample is added, while also allowing the feature of training picture to become more.
Further, described step S2 includes following sub-step：
S2-1；Convolutional neural networks model should be set up first, and the structure of whole model is input layer, convolutional layer, Chi Hua Layer, normalizing layer, convolutional layer normalizes layer, pond layer, full articulamentum, output layer.
1) input layers are, as input, to be input to positive negative sample in convolutional neural networks model；
2) the feature extractions first stage, the convolution kernel size of convolutional layer is 5*5, inputs 3 passages, exports 64 passages, is moved Dynamic step-length is 1, and pond layer is carried out by the way of maximum pond, and window size is 3*3, and step-length is 2, then by obtained feature Figure is normalized；
3) enters feature extraction second stage, and the convolution kernel size of convolutional layer remains 5*5, inputs 64 passages, output 64 Passage, step-length is 1, then pond will be carried out after the characteristic pattern normalization operation after convolution, pond mode, which remains unchanged, takes maximum Chi Hua, window size is 3*3, and step-length is 2；
4) pond result is finally put into full articulamentum by, is finally exported.As shown in Figure 2.
S2-2:Need to carry out just the parameters in network model after the model structure of convolutional neural networks is designed Beginningization, in data initialization, randomness is as high as possible, and convergent speed can be than very fast when so training, and is not easy to be absorbed in The result of local optimum.
S2-3:Right value update in convolutional neural networks model is carried out using BP back propagations, BP back propagation roots The result calculated according to propagated forward is mutually compared with objective result, the difference between two results, i.e. overall error is drawn, according to total Error progressively forward, updates each layer of weights.The method selection gradient descent method of weights is updated at every layer, is declined using gradient Method calculates the best initial weights under error current, obtains updating the weights of front layer successively after best initial weights.Gradient descent method Learning Rate learning rates are arranged between 0.003-0.004.
S2-4:Finally output is using Softmax as two graders, and Softmax is returned in two steps：The first step is in order to obtain One given picture belongs to the evidence (evidence) of some optional network specific digit class, and we are weighted summation to picture pixels value. If there is this pixel very strong evidence to illustrate that this pictures is not belonging to such, then corresponding weights are negative, conversely such as Really this pixel possesses favourable evidence and supports this pictures to belong to this class, then weights are positive numbers.I.e.:
Wherein wiRepresent weight, biThe amount of bias of numeral i classes is represented, the pixel index that j represents given picture x is used for pixel Summation.Then these evidences can be converted into probability y with Softmax functions：
Y=softmax (evidence)
Here Softmax can regard excitation (activation) function as, therefore, give a pictures, it A probable value can be converted into by Softmax functions for each digital goodness of fit.Softmax functions can be defined For：
Softmax (x)=normalize (exp (x))
Deploy the minor on the right of equation, can obtain：
Softmax graders are that the output valve using full articulamentum treats as power exponent evaluation as input value, and input value, These end values of regularization again.This power operation represents that bigger evidence corresponds to the multiplier weight inside bigger hypothesized model Value.
Conversely, possessing less evidence means to possess smaller multiplier coefficients inside hypothesized model.In hypothesized model Weights cannot be 0 value or negative value.Softmax then can regularization these weighted values, their summation is equal to 1, with This one effective probability distribution of construction.
S2-5:After the result of a probability distribution is obtained using Softmax graders, by these results and final mark Label are compared, and determine a threshold value T by comparing, and the probable value that the threshold value represents to work as in Softmax training results is more than T When, then it will judge in input picture it is to include bridge, if the probable value in training result is less than T, that judges input It is not include bridge in picture.
S2-6:Using step S1-1, the positive negative sample that S1-2 is drawn is rolled up after these samples are put into each layer parameter of initialization Product neural network model, is iterated training.In the training process, it will not take and disposably all put all training samples Enter the strategy in model, can so make mode input excessive, calculating the slow and general equipment of meeting can not support yet.
Therefore, the strategy of circuit training can be taken in training process, is randomly selected every time from all samples pictures certain The picture of quantity is trained, and batch size batch_size sizes chosen here are 128, are then randomly selected same amount of Other samples are trained, and in constantly cyclic process (cycle-index is set to 10000), gradually update convolutional neural networks Weights in model, training speed and efficiency can not only be improved by so doing, while accuracy rate also can be higher.
Further, described step S3 includes following sub-step：
S3-1:Remote sensing image to be detected is chosen first, and for general remote sensing images, the size of remote sensing images is all non- Chang great, therefore 1/8th or 1/10th of a remote sensing image to be detected can be intercepted in the present invention, it is detected every time In a part then after detect other parts again, burden of such detector in detection can be smaller, it is easier to counts Calculate, efficiency also can be higher.
S3-2:Remote sensing image to be detected is chosen, is scanned by w*h size windows since the upper left corner of remote sensing image, Transversal scanning step-length is w/2, and when the low order end of scanning to picture to be detected, one is moved down according to longitudinal scanning step-length h/2 OK, then the step scan since Far Left according to horizontal w/2, a completely remote sensing image is scanned successively.
S3-3:Each step scanning all obtains a candidate window, in scanning, by the upper left corner of each candidate window Position coordinates is recorded, and is used as the positional information of candidate's picture.Therefore, each candidate window should be candidate's window comprising information The picture region image that mouth is represented, top left co-ordinate (x, y), wide height (w, h), i.e. (image, x, the y, w, h) of candidate's picture.
Further, described step S4 includes following sub-step：
S4-1:The candidate window that step S3 is obtained trains the input of obtained detector as step S2, to all Candidate window is detected, records the candidate's picture for being judged as including bridge by detector, and preserve these candidate windows.
S4-2:The positional information that the candidate window of preservation is included is extracted, then the basis on picture to be detected The positional information of candidate window marks the image-region representated by candidate window, is finally completed to remote sensing images Bridge position Detection work.
Due to the use of CPU calculating speeds being slow for GPU, therefore GPU has been used to be trained finally And calculating, this causes training speed to be greatly improved, while detection efficiency is also greatly improved.
Described above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (6)

1. a kind of remote sensing images Bridges Detection based on convolutional neural networks, it is characterised in that：Including step：
S1：Training sample is gathered and pretreatment；
S1-1：The remote sensing images for including bridge area are chosen, manual interception size size is the bridge of w*h sizes on remote sensing images Beam picture；
S1-2：The region of bridge is not included on remote sensing images, interception size size is w*h picture, is used as the negative of detector Sample is trained；
S1-3：The positive negative sample obtained in selecting step S1-1, S1-2, on the premise of picture w*h sizes are kept, is aligned Negative sample picture carries out flip horizontal, change of scale, translation transformation, rotation transformation and whitening operation；
S2：Convolutional neural networks training pattern is set up, detector is obtained；
S2-1：Convolutional neural networks model is set up, and the parameters in convolutional neural networks model are initialized；
S2-2：The positive negative sample that step S1-1, S1-2 is obtained is put into the convolutional neural networks model that S2-1 is obtained, and is iterated Training；
S3：Detect the pretreatment of sample：
Remote sensing image to be detected is chosen, is scanned by w*h size windows since the upper left corner of remote sensing image, transversal scanning step A length of w/2, when the low order end of scanning to picture to be detected, a line is moved down according to longitudinal scanning step-length h/2, then from most left While starting the step scan according to horizontal w/2, a completely remote sensing image is scanned successively；Record the candidate that each step scanning is all obtained The position coordinates in the window upper left corner, is used as the positional information of candidate's picture；
S4：Detection sample input detector obtains result；
S4-1：The candidate window that step S3 is obtained trains the input of obtained detector as step S2, to all candidates Window is detected, records the candidate's picture for being judged as including bridge by detector, and preserve these candidate windows；
S4-2：The positional information that the candidate window of preservation is included is extracted, then according to candidate on picture to be detected The positional information of window marks the image-region representated by candidate window, is finally completed the inspection to remote sensing images Bridge position Survey work.
2. remote sensing images Bridges Detection according to claim 1, it is characterised in that：The step S1-1 is in interception bridge When beam picture, the obvious picture of bridge feature should be chosen, while also to intercept comprising bridge, but feature is not obvious, It is blocked or more fuzzy bridge picture.
3. remote sensing images Bridges Detection according to claim 1, it is characterised in that：The volume that the step S2-1 is set up Product neural network model includes input layer, convolutional layer, pond layer, convolutional layer, pond layer, full articulamentum and output layer；
1) input layers are, as input, to be input to positive negative sample in convolutional neural networks model；
2) the feature extractions first stage：The convolution kernel size of convolutional layer is 5*5, inputs 3 passages, exports 64 passages, mobile step A length of 1；Pond layer is carried out by the way of maximum pond, and window size is 3*3, and step-length is 2, then enters obtained characteristic pattern Row normalization；
3) enters feature extraction second stage：The convolution kernel size of convolutional layer remains 5*5, inputs 64 passages, and output 64 is led to Road, step-length is 1, then pond will be carried out after the characteristic pattern normalization operation after convolution, pond mode, which remains unchanged, takes maximum pond Change, window size is 3*3, and step-length is 2；
4) pond result is finally put into full articulamentum by, is finally exported.
4. remote sensing images Bridges Detection according to claim 1, it is characterised in that：The volume that the step S2-1 is set up Right value update in product neural network model is carried out using BP back propagations；Under every layer of method selection gradient for updating weights Drop method；The Learning Rate learning rates of the gradient descent method are arranged between 0.003-0.004.
5. remote sensing images Bridges Detection according to claim 1, it is characterised in that：The volume that the step S2-1 is set up The last output of product neural network model is using Softmax as two graders, and Softmax is returned in two steps：The first step in order to The evidence that a given picture belongs to some optional network specific digit class is obtained, summation is weighted to picture pixels value；If this picture There is element very strong evidence to illustrate that this pictures is not belonging to such, then corresponding weights are negative, if this opposite pixel Possessing favourable evidence supports this pictures to belong to this class, then weights are positive numbers；I.e.：
<mrow> <msub> <mi>evidence</mi> <mi>i</mi> </msub> <mo>=</mo> <munder> <mi>&amp;Sigma;</mi> <mi>j</mi> </munder> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <msub> <mi>x</mi> <mi>j</mi> </msub> <mo>+</mo> <msub> <mi>b</mi> <mi>i</mi> </msub> </mrow>
evidenceiRepresent that given picture belongs to the evidence of i classes；Wherein wiRepresent weight, biRepresent the amount of bias of numeral i classes, j The pixel index for representing given picture x is summed for pixel；Then these evidences can be converted into probability with Softmax functions y：
Y=softmax (evidence)
Wherein, Softmax is an excitation function, therefore, gives a pictures, it is for each digital goodness of fit quilt Softmax functions are converted into a probable value；Softmax functions are defined as：
Softmax (x)=normalize (exp (x))
Deploy the minor on the right of equation, obtain：
<mrow> <mi>s</mi> <mi>o</mi> <mi>f</mi> <mi>t</mi> <mi>max</mi> <msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mi>exp</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>&amp;Sigma;</mi> <mi>j</mi> </msub> <mi>exp</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>;</mo> </mrow>
After the result of a probability distribution is obtained using Softmax graders, result is compared with final label, and A threshold value T is determined by comparing, the threshold value is represented when the probable value in Softmax training results is more than T, then judged defeated Enter and bridge is included in picture；If the probable value in training result is less than T, that judges not including bridge in input picture.
6. remote sensing images Bridges Detection according to claim 1, it is characterised in that：Iteration in the step S2-2 In training process, the strategy of circuit training is taken；A number of picture is randomly selected from all samples pictures every time to carry out Training, the batch_size sizes of selection are 128, then randomly select other same amount of samples and are trained, continuous In ground cyclic process, the weights in convolutional neural networks model are gradually updated.
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Application publication date: 20170818