An Open Set Recognition Method for SAR Targets Based on Multitask Learning

Ma, Xiaojie; Ji, Kefeng; Zhang, Linbin; Feng, Sijia; Xiong, Boli; Kuang, Gangyao

doi:10.1109/lgrs.2021.3079418

Cited by 36 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can see it is able to deal with the continuous angle, but the generated images are not as clear as the conditional GANs. The proposed CVAE-GAN performs well on both the image quality and the conditional generation, as shown in Figures 7,9 and 12. With regard to the SAR target recognition with unknown classes, the open set recognition method proposed in [28] is also tested. The test condition is the same as that in Section 3.3.…”

Section: Discussionmentioning

confidence: 99%

“…The test condition is the same as that in Section 3.3. The network is built according the Figure 1 in [28] and the input images are cropped into the size of 64 × 64 as [28] did. The network is trained in 300 epochs and all the networks with different epochs are used for the OSR test.…”

Section: Discussionmentioning

confidence: 99%

“…These methods don't exploit the strong ability of feature extraction in DL. Recently, a OSR method for SAR-ATR is proposed based on Generative Adversarial Network (GAN) [28]. A classification branch is added to the discriminator, so the discriminator can provide the target class as well as the score of probability that the target belongs to the known class.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the latent variable obtained via the encoder network can act as the representation of SAR images and reflects some attributes of the target. The network in [28] is incapable of the interpretability.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

Feng

Guo

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

Feng

Guo

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This structure is widely used in the research of multi-view SAR images [28]. At present, there are three main research targets of deep-learning based SAR image target recognition methods: aircraft target [29], ship target [30], and vehicle target [31]. All these researches need the support of abundant target data.…”

Section: Introductionmentioning

confidence: 99%

SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

Feng¹,

Ji²,

Zhang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Identification of untrained class data using neuron clusters

Lee

Chae

2023

Neural Comput & Applic

View full text Add to dashboard Cite

Convolutional neural networks (CNNs), a representative type of deep neural networks, are used in various fields. There are problems that should be solved to operate CNN in the real-world. In real-world operating environments, the CNN’s performance may be degraded due to data of untrained types, which limits its operability. In this study, we propose a method for identifying data of a type that the model has not trained on based on the neuron cluster, a set of neurons activated based on the type of input data. In experiments performed on the ResNet model with the MNIST, CIFAR-10, and STL-10 datasets, the proposed method identifies data of untrained and trained types with an accuracy of 85% or higher. The more data used for neuron cluster identification, the higher the accuracy; conversely, the more complex the dataset's characteristics, the lower the accuracy. The proposed method uses only the information of activated neurons without any addition or modification of the model’s structure; hence, the computational cost is low without affecting the classification performance of the model.

show abstract

An Open Set Recognition Method for SAR Targets Based on Multitask Learning

Cited by 36 publications

References 16 publications

Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

Identification of untrained class data using neuron clusters

Contact Info

Product

Resources

About