CN114220003A - Multi-target unsupervised domain self-adaption method for large-range ground object segmentation - Google Patents
Multi-target unsupervised domain self-adaption method for large-range ground object segmentation Download PDFInfo
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Abstract
A multi-target unsupervised domain self-adaptive method for large-range ground object segmentation comprises the following steps: step 1: selecting data of one of a plurality of fields from the obtained large-range remote sensing image, cutting the data to manufacture a source field data set after marking, and directly cutting images of other fields to manufacture a plurality of target field data sets; step 2: putting the source domain data set and the target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a segmentation model; and step 3: putting a plurality of target domain data sets into a segmentation model, and making a target domain image with a high-confidence pseudo label into a pseudo source domain data set through entropy minimum sorting; and 4, step 4: putting the pseudo source domain data set and the plurality of target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a final segmentation model; and 5: and putting all the remote sensing images into a model to obtain a segmentation result.
Description
Technical Field
The invention relates to the technical field of semantic segmentation in remote sensing images, in particular to a multi-target unsupervised domain self-adaptive method for large-range ground object segmentation.
Background
Land resource utilization and human activities are closely related, and accurate acquisition of land utilization information has great significance in better environmental protection, development of dominant economic industries, disaster prevention and the like. With the development of remote sensing technology and deep learning, the method for segmenting the ground features on the remote sensing image through the semantic segmentation algorithm is regarded as an efficient and convenient scheme.
The semantic segmentation algorithm can learn better feature extraction capability under the supervision of a large amount of data, but the trained model is not always effective. The acquisition of the remote sensing image is interfered by a plurality of aspects, so that the difference exists in the distribution of the characteristics, and the difference of the characteristics makes the semantic segmentation model unable to obtain the expected result on the untrained image. In such a scenario, the image with the label for training may be referred to as a source domain image, and the image without the label may be referred to as a target domain image. Since the drastic changes of objects and scenes captured in remote sensing images are difficult to segment through a single model trained on data sets collected at several specific sites, the segmentation of the ground objects in remote sensing images in a wide range becomes a difficult task.
In order to solve the problem, the traditional unsupervised domain self-adaptive method enables the model to obtain better segmentation results on data in different fields in a mode of aligning the characteristics of a source domain and a target domain. However, these domain adaptive methods are all established under the condition of a single source domain and a single target domain, which still has a great problem in practical use, a large-range remote sensing image may have multiple domains, all target domains are divided into one target domain for domain adaptive learning, and the performance of the obtained segmentation model is far inferior to that of the segmentation model obtained by single source-single target domain adaptive learning. And aligning multiple target domain fields with the source domain respectively is costly and long-term. Therefore, designing a method which can align a plurality of target domains and does not reduce the model performance is an important premise for realizing the ground feature segmentation of the large-range remote sensing image.
According to the method, the source domain and the target domains are directly aligned by constructing the multi-branch unsupervised domain self-adaptive model, and the characteristics are further aligned by constructing the pseudo source domain data set through entropy minimization. The method has the advantages that the segmentation model learns the characteristics of multiple fields with low cost, the problem of field deviation between the source field and multiple target fields is well relieved, and therefore the effect of the segmentation model on the large-range remote sensing image is further improved.
Disclosure of Invention
The invention aims to realize the ground feature segmentation of a large-range remote sensing image, and provides a model which can directly align a source and a plurality of target domains, so that the segmentation model can obtain a better segmentation result on data of the plurality of domains with lower cost.
A multi-target unsupervised domain self-adaptive method for large-range ground object segmentation comprises the following steps:
step 1: selecting data of any one of a plurality of fields, cutting the data after marking to manufacture a source field data set, and directly cutting images of other fields except the selected field to manufacture a plurality of target field data sets;
step 2: putting the source domain data set and the target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a segmentation model;
and step 3: inputting a plurality of target domain data sets into a segmentation model, and making a target domain image with a high-confidence pseudo label into a pseudo source domain data set through entropy minimum sorting;
and 4, step 4: putting the pseudo source domain data set and the plurality of target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a final segmentation model;
and 5: putting all remote sensing images into a model to obtain a result;
in step 1, when the data of the selected domain is marked, the number of feature types recognized by the model is the same as the number of marked types.
In step 2, when training the multi-branch unsupervised domain adaptive model to obtain a segmentation model, the method includes the following steps:
1) constructing a multi-branch unsupervised domain self-adaptive model;
2) taking the source domain data set and the plurality of target domain data sets as input, and training until the model performance reaches the optimum;
in step 1), the multi-branch unsupervised domain adaptive model is composed of a feature extractor, an invariant feature classifier, K target classifiers, K branch modules and K fine-grained discriminators (the value of K is equal to the number of target domains). The system comprises a feature extractor, a K target classifiers and K branch modules, wherein the K target classifiers and the K branch modules are connected behind the feature extractor, the K branch modules are respectively connected with K corresponding fine-grained discriminators, and meanwhile, the outputs of the K branch modules are also connected with the invariant feature classifiers.
In step 2), training each module in the multi-branch unsupervised domain self-adaptive model is supervised by different data, wherein the feature extractor and the invariant feature classifier are trained through all data; the kth target classifier, the kth branching module and the kth fine-grain discriminator are trained on the source domain dataset and the kth target domain dataset (K ═ 1,2, …, K).
The loss function used to train the feature extractor is defined as follows:
wherein L issegIs the loss of segmentation from the source domain data,
and
are the countermeasure loss and classification loss generated by the target domain data. H. W, C are the height, width and number of categories, respectively, of the input image.
Is the domain label of the target domain image.
Is the predicted probability that the target domain image will be generated after passing through the discriminator.
Is the resulting prediction probability of the target domain image on the kth target classifier. n issAnd
is the number of samples of the source domain and the kth target domain.
And
are weighting factors that control the impact on the immunity to loss and classification loss.
The dimensionality of the domain label is the same as the number of the ground object categories, and the value of the domain label represents the probability that the model prediction pixel point belongs to a certain category.
The data flow of the source domain data set in the multi-branch unsupervised domain adaptive model comprises the following steps:
1) generating a feature map from the source domain image through a feature extractor;
2) extracting invariant features from the source domain feature map through all branch modules, inputting the generated invariant features into an invariant feature classifier to generate a prediction result, and calculating segmentation loss;
3) inputting the source domain invariant features into a discriminator, and training the discriminator;
the data flow of the multiple target domain data sets in the multi-branch unsupervised domain adaptive model comprises the following steps:
1) generating a feature map from the target domain image through a feature extractor;
2) extracting invariant features from a feature map generated by the image of the kth target domain through a kth branch module, inputting the generated invariant features into an invariant feature classifier to generate a pseudo label, and inputting the invariant features into a kth discriminator to calculate the countermeasure loss;
3) inputting a feature map generated by the image of the kth target domain into a kth target classifier, and calculating classification loss;
4) inputting the invariant features of the kth target domain into a kth discriminator, and training and inputting the kth discriminator;
in step 3, the target domain image is input into the model to obtain a prediction result, the image segmentation conditions are sequenced by calculating the entropy of the prediction result, and the image with high confidence coefficient is extracted.
In step 4, the target domain label graph with high confidence coefficient and the corresponding target domain image are used as a pseudo source domain, and the pseudo source domain and the target domain image are input into a multi-branch unsupervised domain adaptive model for training.
In step 5, all remote sensing images are cut into small pictures which can be identified by the model, and the model is input to obtain a segmentation result.
Compared with the prior art, the invention has the following technical effects:
(1) the invention is a multi-target unsupervised domain self-adaptive model, and only the data of one domain needs to be marked, so that the knowledge of the source domain can be migrated to other domains at one time. The segmentation model is better popularized, and meanwhile, the labor cost for realizing ground object segmentation in a large-range remote sensing image is reduced;
(2) according to the invention, through designing a multi-branch structure, firstly, a source domain and different target domains are aligned on different branches, so that the target domains are not aligned directly, and the condition that the result is poor due to overlarge gaps among the target domains is avoided; secondly, the branch module in the multi-branch structure separates the domain characteristics from the domain invariant characteristics, so that the domain characteristics are reserved, more effective segmentation can be realized, and meanwhile, the domain invariant characteristics enable the model to be aligned with the characteristics of different domains;
(3) according to the method, the pseudo labels with high confidence coefficient are obtained through entropy minimization, and the target domain images with the pseudo labels are aligned with other target domain images, so that the interior of the field is also aligned, and the performance of the model after intra-domain alignment is further improved;
drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of source domain data flow of the present invention;
FIG. 3 is a flow chart of a target domain data flow in the present invention;
FIG. 4 is a diagram of a network architecture according to the present invention;
Detailed Description
The invention aims to realize the segmentation of the ground features in a wide-range remote sensing image, and provides a model which can directly align a source and a plurality of target domains, so that the segmentation model can obtain a better segmentation result on data of a plurality of domains with lower cost.
A multi-target unsupervised domain self-adaptive method for dividing ground objects in a large-range remote sensing image comprises the following steps:
step 1: selecting data of one of a plurality of fields from the obtained large-range remote sensing image, cutting the data to manufacture a source field data set after marking, and directly cutting images of other fields to manufacture a plurality of target field data sets;
step 2: putting the source domain data set and the target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a segmentation model;
and step 3: inputting a plurality of target domain data sets into a segmentation model, and making a target domain image with a high-confidence pseudo label into a pseudo source domain data set through entropy minimum sorting;
and 4, step 4: putting the pseudo source domain data set and the plurality of target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a final segmentation model;
and 5: putting all remote sensing images into a model to obtain a result;
in step 2, when training the multi-branch unsupervised domain adaptive model to obtain a segmentation model, the method includes the following steps:
1) constructing a multi-branch unsupervised domain self-adaptive model;
2) taking the source domain data set and the plurality of target domain data sets as input, and training until the model performance reaches the optimum;
in step 1), the multi-branch unsupervised domain adaptive model is composed of a feature extractor, an invariant feature classifier, K target classifiers, K branch modules and K fine-grained discriminators (the value of K is equal to the number of target domains). The system comprises a feature extractor, a K target classifiers and K branch modules, wherein the K target classifiers and the K branch modules are connected behind the feature extractor, the K branch modules are respectively connected with K corresponding fine-grained discriminators, and meanwhile, the outputs of the K branch modules are also connected with the invariant feature classifiers.
In step 2), training of each module in the multi-branch unsupervised domain adaptive model is affected by different data. Wherein the training of the feature extractor and invariant feature classifier is affected by all data; the kth target classifier, the kth branch module and the kth fine-grain discriminator training are influenced by the source domain dataset and the kth target domain dataset (K ═ 1,2, ·, K).
The loss function used to train the feature extractor is defined as follows:
wherein L issegIs the loss of segmentation from the source domain data,
and
are the countermeasure loss and classification loss generated by the target domain data. H. W, C are the height, width and number of categories, respectively, of the input image.
Is the domain label of the target domain image.
Is the predicted probability that the target domain image will be generated after passing through the discriminator.
Is the resulting prediction probability of the target domain image on the kth target classifier. n issAnd
is the number of samples of the source domain and the kth target domain.
And
are weighting factors that control the impact on the immunity to loss and classification loss.
The data flow of the source domain data set in the multi-branch unsupervised domain adaptive model comprises the following steps:
1) generating a feature map from the source domain image through a feature extractor;
2) extracting invariant features from the source domain feature map through all branch modules, inputting the generated invariant features into an invariant feature classifier to generate a prediction result, and calculating segmentation loss;
3) inputting the source domain invariant features into a discriminator, and training the discriminator;
the data flow of the multiple target domain data sets in the multi-branch unsupervised domain adaptive model comprises the following steps:
1) generating a feature map from the target domain image through a feature extractor;
2) extracting invariant features from a feature map generated by the image of the kth target domain through a kth branch module, inputting the generated invariant features into an invariant feature classifier to generate a pseudo label, and inputting the invariant features into a kth discriminator to calculate the countermeasure loss;
3) inputting a feature map generated by the image of the kth target domain into a kth target classifier, and calculating classification loss;
4) inputting the invariant features of the kth target domain into a kth discriminator, and training and inputting the kth discriminator;
in step 3, the target domain image is input into the model to obtain a prediction result, the image segmentation conditions are sequenced by calculating the entropy of the prediction result, and the image with high confidence coefficient is extracted.
In step 4, the target domain label graph with high confidence coefficient and the corresponding target domain image are used as a pseudo source domain, and the pseudo source domain and the target domain image are input into a multi-branch unsupervised domain adaptive model for training.
In step 5, all remote sensing images are cut into small pictures which can be identified by the model, and the model is input to obtain a segmentation result.
Example (b):
the invention provides a method for segmenting ground objects in a large-range remote sensing image on the remote sensing image, which is carried out according to the following modes:
step 1: acquiring data of one of a plurality of fields from a large-range remote sensing image, and marking different ground object categories through ENVI; cutting the obtained image and the corresponding label to prepare a source domain data set, and directly cutting the images in other fields to prepare a plurality of target domain data sets; the cropped image size is 512 × 512 pixels.
Step 2: and constructing a multi-branch unsupervised domain self-adaptive model, wherein the multi-branch unsupervised domain self-adaptive model consists of a feature extractor, an invariant feature classifier, K target classifiers, K branch modules and K fine-grained discriminators (the value of K is equal to the number of the target domains).
And step 3: a loss function in the model is defined. The loss function used to train the feature extractor is defined as follows:
wherein L issegIs the loss of segmentation from the source domain data,
and
are the countermeasure loss and classification loss generated by the target domain data. H. W, C are the height, width and number of categories, respectively, of the input image.
Is the domain label of the target domain image.
Is the predicted probability that the target domain image will be generated after passing through the discriminator.
Is the object domain image is classified at the kth objectThe resulting prediction probabilities on the machine. n issAnd
is the number of samples of the source domain and the kth target domain.
And
are weighting factors that control the impact on the immunity to loss and classification loss.
And 4, step 4: the model is trained using the source domain dataset and the plurality of target domain datasets as inputs. The data flow of the source domain data set in the multi-branch unsupervised domain adaptive model is as follows: generating a feature map from the source domain image through a feature extractor; extracting invariant features from the source domain feature map through all branch modules, inputting the generated invariant features into an invariant feature classifier to generate a prediction result, and calculating segmentation loss; and inputting the source domain invariant features into a discriminator and training the discriminator.
The data flow of the multiple target domain data sets in the multi-branch unsupervised domain adaptive model is as follows: generating a feature map from the target domain image through a feature extractor; extracting invariant features from a feature map generated by the image of the kth target domain through a kth branch module, inputting the generated invariant features into an invariant feature classifier to generate a pseudo label, and inputting the invariant features into a kth discriminator to calculate the countermeasure loss; inputting a feature map generated by the image of the kth target domain into a kth target classifier, and calculating classification loss; inputting the invariant features of the kth target domain into a kth discriminator, and training and inputting the kth discriminator;
and 5: the image of the target domain is input into the model to obtain pseudo labels, the confidence degrees of the image pseudo labels of the target domain are sequenced through entropy minimization, the target domain images with high confidence degrees are selected to be made into a pseudo source domain data set, the pseudo source domain data set and the images of the target domain are input into the multi-branch unsupervised domain self-adaptive model to be trained again, and a final segmentation model is obtained.
Step 6: and cutting all the remote sensing images into small pictures which can be identified by the model, and inputting the small pictures into the model to obtain a segmentation result.
Claims (9)
1. A multi-target unsupervised domain self-adaptive method for large-range ground object segmentation comprises the following steps:
step 1: selecting data of any one of a plurality of fields from the obtained large-range remote sensing image, cutting the data to manufacture a source field data set after marking, and directly cutting images of other fields except the selected field to manufacture a plurality of target field data sets;
step 2: putting the source domain data set and the target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a segmentation model;
and step 3: inputting a plurality of target domain data sets into a segmentation model, and making a target domain image with a high-confidence pseudo label into a pseudo source domain data set through entropy minimum sorting;
and 4, step 4: putting the pseudo source domain data set and the plurality of target domain data sets into a multi-branch unsupervised domain self-adaptive model for training to obtain a final segmentation model;
and 5: and putting all the remote sensing images into a model to obtain a segmentation result.
2. The method according to claim 1, wherein the step 2, when training the multi-branch unsupervised domain adaptive model to obtain the segmentation model, comprises the following steps:
1) constructing a multi-branch unsupervised domain self-adaptive model;
2) and taking the source domain data set and the plurality of target domain data sets as input, and training until the model performance reaches the optimum.
3. The method according to claim 2, characterized in that in step 1), the multi-branch unsupervised domain adaptive model is composed of a feature extractor, an invariant feature classifier, K target classifiers, K branch modules and K fine-grained discriminators; the feature extractor is connected with K target classifiers and K branch modules, and the K branch modules are respectively connected with K corresponding fine-grained discriminators.
4. The method according to claim 2, wherein in step 2), each module in the multi-branch unsupervised domain adaptive model is trained under different data supervision, wherein the feature extractor and the invariant feature classifier are trained by all data; the kth target classifier, the kth branching module and the kth fine-grain discriminator are trained on the source domain dataset and the kth target domain dataset (K ═ 1,2, …, K).
5. The method of claim 4, wherein the loss function used to train the feature extractor is defined as follows:
wherein L issegIs the loss of segmentation from the source domain data,
and
is the contrast loss and classification loss produced by the target domain data, H, W, C is the height, width and number of classes of the input image,
is a domain label of the target domain image,
is the predicted probability that the target domain image will be generated after passing through the discriminator,
is the resulting prediction probability of the target domain image on the kth target classifier,
is the number of samples of the kth target domain,
and
are weighting factors that control the impact on the immunity to loss and classification loss.
6. The method according to one of claims 2 to 5, wherein the data flow of the source domain data set in the multi-branch unsupervised domain adaptation model comprises the following steps:
1) generating a feature map from the source domain image through a feature extractor;
2) extracting invariant features from the source domain feature map through all branch modules, inputting the generated invariant features into an invariant feature classifier to generate a prediction result, and calculating segmentation loss;
3) and inputting the source domain invariant features into a discriminator and training the discriminator.
7. The method according to one of claims 2 to 5, wherein the data flow of the multiple target domain data sets in the multi-branch unsupervised domain adaptation model comprises the following steps:
1) generating a feature map from the target domain image through a feature extractor;
2) extracting invariant features from a feature map generated by the image of the kth target domain through a kth branch module, inputting the generated invariant features into an invariant feature classifier to generate a pseudo label, and inputting the invariant features into a kth discriminator to calculate the countermeasure loss;
3) inputting a feature map generated by the image of the kth target domain into a kth target classifier, and calculating classification loss;
4) the invariant features of the kth target domain are input into a kth discriminator, and training is input into the kth discriminator.
8. The method according to claim 1, wherein in the step 3, the target domain image is input into the model to obtain a prediction result, and the image segmentation conditions are sorted by calculating entropy of the prediction result to extract the image with high confidence.
9. The method according to claim 1, wherein in step 4, the target domain label map with high confidence and the corresponding target domain image are used as a pseudo source domain, and the pseudo source domain and the target domain image are input into a multi-branch unsupervised domain adaptive model for training.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082725A (en) * | 2022-05-17 | 2022-09-20 | 西北工业大学 | Multi-source domain self-adaption method based on reliable sample selection and double-branch dynamic network |
| CN115082725B (en) * | 2022-05-17 | 2024-02-23 | 西北工业大学 | Multi-source domain self-adaption method based on reliable sample selection and double-branch dynamic network |
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