CN116310581A - Semi-supervised change detection flood identification method - Google Patents
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Abstract
The invention discloses a semi-supervised change detection flood identification method, which comprises the following steps: s1, preprocessing an acquired sentinel first-number image pair, and manufacturing a flood change water body label; s2, constructing a semi-supervised change detection network; s3, calculating total loss of unsupervised loss and supervised loss, automatically calculating gradients of all parameters in a supervised learning network by using a deep learning framework, optimizing the parameters by using an optimizer, meeting set conditions, and storing network parameters of the supervised learning network with highest verification accuracy; s4, inputting the first-sentinel images before and after flood in the disaster area into a semi-supervised change detection network for training, and obtaining a flood change water prediction result. According to the invention, the flood water body characteristics are learned in a large number of non-label image pairs, so that the monitoring learning network can better identify flood by only needing less label data, obtain higher identification accuracy and other evaluation indexes, and save a large number of time and resources consumed by manual labeling.
Description
Technical Field
The invention relates to the field of remote sensing big data, in particular to a semi-supervised change detection flood identification method.
Background
In recent years, the annual loss of life and property of people caused by flood disasters is huge, and the work of post-disaster repair and disaster damage assessment has higher requirements on a remote sensing flood monitoring method. At present, a flood identification method based on a deep learning change monitoring technology is superior to a traditional remote sensing image change detection method in disaster identification effect, and a flood identification method based on the deep learning change monitoring has become the mainstream. However, the data set required for training the deep learning flood monitoring model is high in acquisition cost and high in difficulty, and how to train the neural network with good flood detection effect on a small-scale data set is a problem to be solved urgently. Recently, semi-supervised learning has been impressive in semantic segmentation tasks on very small-scale datasets, and the effect equivalent to that of traditional convolutional neural networks using full dataset supervised training can be achieved with only 10% of the data volume of the original dataset. Semi-supervised learning learns image features in unlabeled images through non-supervised training in addition to supervised training with a small number of samples through its unique semi-supervised training method.
Disclosure of Invention
The invention aims to: the invention aims to provide a semi-supervised change detection flood identification method capable of utilizing a large number of unlabeled sentinel images to learn the characteristics of a flood water body, so that the dependence of flood identification on manual label tags is greatly reduced.
The technical scheme is as follows: the flood identification method provided by the invention comprises the following steps:
s1, acquiring a pair of first-number images of a front guard and a rear guard of a flood, preprocessing the pair of first-number images of the guard, and manufacturing a flood change water body label; dividing a data set of the flood change water body label into a training set, a verification set and a test set according to a proportion;
s2, constructing a semi-supervised change detection network, wherein the semi-supervised change detection network comprises an unsupervised learning network and a supervised learning network;
s3, training an unsupervised learning network through an unsupervised loss function, and training the supervised learning network through a supervised loss function; calculating the total loss of the unsupervised loss and the supervised loss, automatically calculating the gradient of each parameter in the supervised learning network by using a deep learning framework after the total loss is obtained, optimizing the parameters by using an optimizer until the set condition is met, and storing the network parameters of the supervised learning network with the highest verification precision to finish the training of the semi-supervised change detection network;
s4, inputting the first images of the sentry before and after the flood in the disaster area into a semi-supervised change detection network for training, and performing forward propagation only in a supervised learning network to obtain a flood change water body prediction result.
In step S1, the first-number image pair of the sentry before and after the flood is obtained is subjected to radiometric calibration, geometric correction and logarithmic conversion pretreatment.
Further, in step S1, the implementation process of making the flooding change water body label is as follows: the unique tone, shape and texture characteristics of the water body in the synthetic aperture radar image are utilized through visual interpretation, the dual-time-phase remote sensing image is marked by marking software, and a binary change result graph is obtained, wherein the flooding area is marked as white, and the unchanged area is marked as black.
Further, in step S2, the implementation steps of the unsupervised learning network are as follows:
s211, setting a first-number image of a sentinel before flood occurs as I a The first image of the sentinel after flood is I b Ith flood initiation in tag-free datasetThe first image of a lifelong sentinel is called
The first image of the sentinel after the occurrence of the ith flood is called +.>
And->
The characteristics are obtained after the encoding by the encoder>
S212, calculating characteristics through a difference module
Absolute difference of +.>
Acquiring differences in image characteristics before and after flood occurrence; the absolute difference is then +>
Inputting a spatial pyramid pooling layer to obtain characteristics +.>
Differential features on different scales +.>
S213, for difference characteristics
Performing disturbance processing, the disturbance processing comprising: random noise processing, random mask processing and random temporary back processing;
the random noise processing process is as follows: first generate an AND
Noise tensors P with the same dimension are set to be subjected to uniform distribution on (-0.3, 0.3), and the noise tensors P and +.>
Multiplying the corresponding elements and then adding +.>
Adding to obtain the random noise difference characteristic +.>
The random mask processing process is as follows: firstly, generating a difference characteristic threshold T, setting T to follow uniform distribution on (0.6,0.9) to generate a mask tensor M,
wherein->
Is->
In the result of channel dimension normalization, mask tensor M and difference feature +.>
Multiplying the corresponding elements to obtain the difference characteristic of the random mask +.>
The random temporary back treatment process comprises the following steps: random to differential features
The element value in each channel is assigned to 0, so that the random temporary return difference characteristic is obtained>
S214, the difference features
Random noise variance feature->
Random mask difference feature->
Random transient differential characteristics->
The dimension is reduced while upsampling by the decoder, respectively, to obtain prediction values +.>
Random noise predictor
Random mask predictor +.>
Random transient prediction value->
Further, in step S2, the implementation process of the supervised learning network is as follows: let the first image of the guard before the occurrence of the ith flood in the data set containing the label be called as
The first image of the sentinel after the occurrence of the ith flood is called +.>
The i-th pair of sentinel first image pair corresponding to flood change water body label is called +.>
The encoder-differential module-decoder process is denoted as
The predicted value is expressed as +.>
Then there are:
wherein "-", is 1 "means absolute difference.
In step S3, after the gradient of each parameter in the supervised learning network is automatically calculated and the parameter update is completed by using the deep learning framework, the supervised learning network is adjusted to a verification mode, the first image pair of the sentinel and the label in the verification set are read into the supervised learning network, the verification precision is calculated, and then the next round of training is performed.
Further, in step S3, the unsupervised learning loss function L unsup The expression of (2) is as follows:
where MSE () represents the mean square error function.
The supervised learning loss function L sup The expression of (2) is as follows:
where CE () represents a cross entropy function.
Compared with the prior art, the invention has the following remarkable effects:
the invention constructs a semi-supervised change detection network comprising an unsupervised learning network and a supervised learning network, trains the unsupervised learning network through an unsupervised loss function, trains the supervised learning network through the supervised loss function, automatically calculates the gradient of each parameter in the supervised learning network by using a deep learning framework after obtaining total loss, and finally completes the training of the semi-supervised change detection network; through the characteristic of learning from a large number of non-label sentinel first-number images centering through the non-supervision learning network, the supervised learning network can realize a better flood identification effect by only needing little label data, obtain evaluation indexes such as higher identification precision and the like, and save a large number of time and resources consumed by manual labeling.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 (a) is a schematic diagram of an unsupervised training dataset;
FIG. 2 (b) is a diagram of a supervised training dataset;
FIG. 3 (a) is a schematic diagram of an unsupervised learning network architecture;
FIG. 3 (b) is a schematic diagram of a supervised learning network architecture;
fig. 4 is a schematic diagram of flood change water body recognition results.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
As shown in fig. 1, the flow chart of the present invention is shown, and the specific implementation steps are as follows:
step one, flood water body data set manufacturing based on sentinel first synthetic aperture radar image
Step 11, acquiring a first synthetic aperture radar image of a front guard post and a rear guard post of the flood occurrence
And acquiring a first-sentinel synthetic aperture radar image of the flood inundation area in two times before and after the flood inundation. In order to acquire the required data set, the embodiment acquires the first-number image pairs of the guard before and after the flood season of the Poyang lake in 2017-2019, and totally 6 (3 pairs) of first-number images of the guard, and performs radiometric calibration, geometric correction and logarithmic conversion pretreatment on the first-number image pairs of the guard.
Step 12, making a small amount of flood change water body labels
The semi-supervised change detection network can utilize the feature extraction capability of a large number of unlabeled image training networks, so that only a small number of flood change water body labels are required to be obtained in the embodiment. The unique tone, shape and texture characteristics of the water body in the synthetic aperture radar image are utilized through visual interpretation, the labeling software is used for labeling the double-phase remote sensing image, a binary change result graph is obtained, wherein the flooding flood inundation area is labeled white, and the unchanged area is labeled black, as shown in fig. 2 (a). The sentinel one-number image and the flood change water body label are cut into 256 x 256 sizes through the sliding window, 3578 pairs of change water body samples (here, 3578 pairs of double-phase sentinel one-number images are specifically 3578 pairs of change water body labels and 3578 pairs of double-phase sentinel one-number images for the supervised learning stage of semi-supervised training) and 6296 pairs of double-phase sentinel one-number images (no labels and the non-supervised learning stage of semi-supervised training) are manufactured for the sentinel one-number image, and the data set is shown in fig. 2 (b).
Step 13, partitioning the data set
3578 data set for variable water body tags were calculated as 8:1: the proportion of 1 is divided into a training set, a verification set and a test set.
Step two, constructing a semi-supervised change detection network
Step 21, constructing an unsupervised network
A1 encoder
The encoder is a ResNet50, in particular, all encoders in the semi-supervised change detection network share weights, i.e. the encoders are identical. Let the first image of the sentinel before flood occurs be I a The first image of the sentinel after flood is I b ，I a And I b Is 256 x 256. Specifically, as shown in fig. 3 (a), the i-th image of the data set without the tag (the data set used in this embodiment is 6296 pairs of double-phase first-sentinel images) is called the first-sentinel image before the occurrence of flood
The first image of the sentinel after the occurrence of the ith flood is called +.>
And->
The characteristics with the shape of 64 x 2048 are obtained after the codes are encoded by an encoder
Expressed by the formula:
wherein Encoder () represents the encoding operation.
A2, differential module
The difference module first calculates
Absolute difference->
To obtain the difference in the image characteristics before and after flood occurrence, and then the absolute difference is +.>
Inputting spatial pyramid pooling layer SPP to obtain +.>
Differential features on different scales +.>
Expressed by the formula:
where SPP () represents a spatial pyramid pooling operation.
A3, disturbance treatment
The perturbation process consists of random noise, random masking and random backoff processes.
Random noise processing is specifically the generation of a sum
Noise tensors P with the same dimension are set to be subjected to uniform distribution on (-0.3, 0.3), and the noise tensors P and +.>
Multiplying the corresponding elements and then adding +.>
Adding to obtain the difference characteristic of the random noise>
The random noise processing is formulated as:
wherein, as indicated by the letter, "-represents a matrix dot product operation.
The random mask processing is specifically to generate a difference characteristic threshold T, set T to follow uniform distribution at (0.6,0.9), generate a mask tensor M,
wherein->
Is->
In the result of channel dimension normalization, mask tensor M and difference feature +.>
Multiplying corresponding elements to obtain random mask difference characteristics>
The random masking process is formulated as:
random temporary back processing is concretely random differential feature
The element value in each channel is assigned to 0, so that the random temporary return difference characteristic is obtained>
A4 decoder
The decoder is a transposed convolutional layer. In particular, all decoders in the semi-supervised variation detection network share weights, i.e. the decoders are identical. The decoder will have a difference characteristic of 64×64×2048 shape
Random noise variance feature->
Random mask difference feature->
Random transient differential characteristics->
The dimensions are reduced while upsampling, resulting in a predicted value +.256 in shape>
Random noise predictor +.>
Random mask predictor +.>
Random transient prediction value->
Expressed as by the formula
Wherein Decoder () represents a decoding operation.
A5 loss function
Calculating an unsupervised learning partial loss L unsup The unsupervised learning partial loss function is defined as:
where MSE () represents the mean square error function.
Step 22, constructing a supervised learning network
B1 encoder-differential module-decoder
The data set containing the tag (the data set used in this example is 3578 vs. double whistleThe first-number image pair of the soldier and the first-number image of the soldier before the occurrence of the ith flood in the corresponding 3578 flood change water body label) are called as
The first image of the sentinel after the occurrence of the ith flood is called +.>
The i-th pair of sentinel first image pair corresponding to flood change water body label is called +.>
The encoder-differential module-decoder process in a supervised learning network is represented simply as +.>
The predicted value of the water label of the flood change corresponding to the first image of the ith pair of sentry is expressed as +.>
Expressed by the formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
“││ 1 "means absolute difference.
B2 loss function
Computing supervised learning network lossesL sup The supervised learning network loss function adopts a cross entropy loss function, and is expressed as follows:
where CE () represents a cross entropy function.
Step three, semi-supervised change detection network training
And (3) taking the training set and the unsupervised data set in the data set containing the change water body label obtained in the step (I) as training data, and training the semi-supervised change detection network constructed in the step (II). Specifically, first, the first image pair of the sentry in the unsupervised data set
The non-supervision learning network read into the semi-supervision change detection network, part of the non-supervision data set is shown in fig. 2 (a), and the non-supervision loss L is calculated unsup . Then, the first image pair of the sentry in the training set containing the variable water body label is corresponding to the +.>
The supervised learning network of the semi-supervised change detection network is read in, part of training sets containing change water body labels are shown in the figure 2 (b), and the supervised loss +.>
Then, the total loss L is calculated according to the following calculation formula
L＝L sup +L unsup (13)
After obtaining the total loss L, automatically calculating gradients of encoder and decoder parameters in a supervised learning network and an unsupervised learning network by using a deep learning framework, optimizing the parameters by using an optimizer, adjusting the supervised learning network into a verification mode, reading a sentinel first image pair and a label in the verification set into the supervised learning network, calculating the verification precision, performing the next round of training, and protecting after repeating training for 100 periodsAnd storing network parameters of the corresponding supervised learning network in the period with the highest verification precision, and completing the training of the semi-supervised change detection network. Because the encoders in the supervised and unsupervised learning networks share parameters, as do the decoders, a large number of unlabeled sentinel one-number image pairs are utilized to calculate the unsupervised loss L in the unsupervised learning network unsup After that, the adjustment made to the parameters of the encoder and the decoder still exists in the encoder and the decoder of the supervised learning network, so that the semi-supervised change detection network needs less manually marked flood change water body labels when in training compared with the traditional supervised change detection network, and can realize better flood identification effect under the condition that the flood change water body labels are few.
Step four, semi-supervised change detection network flood identification
And inputting the first images of the front and rear sentries of the flood disaster area into a supervised learning network for training, and performing forward propagation only in the supervised learning network to obtain a flood change water body prediction result, as shown in fig. 4. Experiments prove that 50 sentinel first-number image pairs with the size of 256 x 256 are used for training the semi-supervised change detection network with corresponding labels and 6296 non-label sentinel first-number images, the identification precision of the semi-supervised change detection flood on the test data set is 0.9792, and the IOU of the flood water body change part is 0.8403.
Claims (7)
1. The semi-supervised change detection flood identification method is characterized by comprising the following steps of:
s1, acquiring a pair of first-number images of a front guard and a rear guard of a flood, preprocessing the pair of first-number images of the guard, and manufacturing a flood change water body label; dividing a data set of the flood change water body label into a training set, a verification set and a test set according to a proportion;
s2, constructing a semi-supervised change detection network, wherein the semi-supervised change detection network comprises an unsupervised learning network and a supervised learning network;
s3, training an unsupervised learning network through an unsupervised loss function, and training the supervised learning network through a supervised loss function; calculating the total loss of the unsupervised loss and the supervised loss, automatically calculating the gradient of each parameter in the supervised learning network by using a deep learning framework after the total loss is obtained, optimizing the parameters by using an optimizer until the set condition is met, and storing the network parameters of the supervised learning network with the highest verification accuracy to finish the training of the semi-supervised change detection network;
s4, inputting the first images of the sentry before and after the flood in the disaster area into a semi-supervised change detection network for training, and performing forward propagation only in a supervised learning network to obtain a flood change water body prediction result.
2. The method for identifying the semi-supervised variation detection flood according to claim 1, wherein in the step S1, the first image pair of the sentry before and after the flood is acquired is subjected to radiometric calibration, geometric correction and logarithmic conversion preprocessing.
3. The semi-supervised variation detection flood identification method according to claim 1, wherein in step S1, the implementation process of making the flood variation water body label is as follows: the unique tone, shape and texture characteristics of the water body in the synthetic aperture radar image are utilized through visual interpretation, the dual-time-phase remote sensing image is marked by marking software, and a binary change result graph is obtained, wherein the flooding area is marked as white, and the unchanged area is marked as black.
4. The method for identifying a semi-supervised variation detection flood according to claim 1, wherein in step S2, the non-supervised learning network is implemented as follows:
s211, setting a first-number image of a sentinel before flood occurs as I a The first image of the sentinel after flood is I b The first image of the sentinel before the occurrence of the ith flood in the data set without the tag is called as
The first image of the sentinel after the occurrence of the ith flood is called as/>
And->
The characteristics are obtained after the encoding by the encoder>
S212, calculating characteristics through a difference module
Absolute difference of +.>
Acquiring differences in image characteristics before and after flood occurrence; the absolute difference is then +>
Inputting a spatial pyramid pooling layer to obtain characteristics +.>
Differential features on different scales +.>
S213, for difference characteristics
Performing disturbance processing, the disturbance processing comprising: random noise processing and random mask processingAnd random temporary back processing;
the random noise processing process is as follows: first generate an AND
Noise tensors P with the same dimension are set to be subjected to uniform distribution on (-0.3, 0.3), and the noise tensors P and +.>
Multiplying the corresponding elements and then adding +.>
Adding to obtain the random noise difference characteristic +.>
The random mask processing process is as follows: firstly, generating a difference characteristic threshold T, setting T to follow uniform distribution on (0.6,0.9) to generate a mask tensor M,
wherein->
Is->
In the result of channel dimension normalization, mask tensor M and difference feature +.>
Multiplying the corresponding elements to obtain the difference characteristic of the random mask +.>
The random temporary back treatment process comprises the following steps: random to differential features
The element value in each channel is assigned to 0, so that the random temporary return difference characteristic is obtained>
S214, the difference features
Random noise variance feature->
Random mask difference feature->
Random transient differential characteristics->
The dimension is reduced while upsampling by the decoder, respectively, to obtain prediction values +.>
Random noise predictor +.>
Random mask predictor +.>
Random transient prediction value->
5. The method for identifying semi-supervised variation detection flooding of claim 1, wherein in step S2, the supervised learning network is implemented as follows: is provided withThe first image of the sentinel before the occurrence of the ith flood in the data set of the tag is called as
The first image of the sentinel after the occurrence of the ith flood is called +.>
The i-th pair of sentinel first image pair corresponding to flood change water body label is called +.>
The encoder-differential module-decoder procedure is denoted +.>
The predicted value is expressed as +.>
Then there are:
wherein "-", is 1 "means absolute difference.
6. The method for identifying the semi-supervised variation detection flood according to claim 1, wherein in the step S3, after the gradient of each parameter in the supervised learning network is automatically calculated by using the deep learning framework and the parameter updating is completed, the supervised learning network is adjusted to be in a verification mode, the first image pair of the sentry and the label in the verification set are read into the supervised learning network, the verification precision is calculated, and then the next round of training is performed.
7. The method of identifying a semi-supervised variation detection flood according to claim 1, wherein in step S3, the unsupervised learning loss function L unsup The expression of (2) is as follows:
where MSE () represents the mean square error function;
the supervised learning loss function L sup The expression of (2) is as follows:
where CE () represents a cross entropy function.
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