Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery
Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or <i>unknown</i> objects without confusing them to be one of the known classes. Our goal i...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/492 |
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| author | Adam Cuellar Daniel Brignac Abhijit Mahalanobis Wasfy Mikhael |
| author_facet | Adam Cuellar Daniel Brignac Abhijit Mahalanobis Wasfy Mikhael |
| author_sort | Adam Cuellar |
| collection | DOAJ |
| description | Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or <i>unknown</i> objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. Instead, we introduce a second network (trained using regression) that uses the decision of the primary classifier to produce a class conditional score that indicates whether an input object is indeed a known object. This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. Most importantly, our method does not require any examples of OOD imagery to be used for training the second network. For illustrative purposes, we demonstrate the effectiveness of the proposed method using the CIFAR-10 dataset. Ultimately, our goal is to classify known targets in infra-red images while improving the ability to reject unknown classes. Towards this end, we train and test our method on a public domain medium-wave infra-red (MWIR) dataset provided by the US Army for the development of automatic target recognition (ATR) algorithms. The results of this experiment show that the proposed method outperforms other state-of-the-art methods in rejecting the unknown target types while accurately classifying the known ones. |
| format | Article |
| id | doaj-art-d6a2eafdbf014cdfa93f191b7cd22742 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-d6a2eafdbf014cdfa93f191b7cd227422025-01-24T13:49:07ZengMDPI AGSensors1424-82202025-01-0125249210.3390/s25020492Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor ImageryAdam Cuellar0Daniel Brignac1Abhijit Mahalanobis2Wasfy Mikhael3Center for Research in Computer Vision, University of Central Florida, Orlando, FL 32816-8005, USADepartment of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721-0104, USADepartment of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721-0104, USADepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816-8005, USARecognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or <i>unknown</i> objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. Instead, we introduce a second network (trained using regression) that uses the decision of the primary classifier to produce a class conditional score that indicates whether an input object is indeed a known object. This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. Most importantly, our method does not require any examples of OOD imagery to be used for training the second network. For illustrative purposes, we demonstrate the effectiveness of the proposed method using the CIFAR-10 dataset. Ultimately, our goal is to classify known targets in infra-red images while improving the ability to reject unknown classes. Towards this end, we train and test our method on a public domain medium-wave infra-red (MWIR) dataset provided by the US Army for the development of automatic target recognition (ATR) algorithms. The results of this experiment show that the proposed method outperforms other state-of-the-art methods in rejecting the unknown target types while accurately classifying the known ones.https://www.mdpi.com/1424-8220/25/2/492infra-redATRtarget classificationunknown rejectionOODopen-set recognition |
| spellingShingle | Adam Cuellar Daniel Brignac Abhijit Mahalanobis Wasfy Mikhael Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery Sensors infra-red ATR target classification unknown rejection OOD open-set recognition |
| title | Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery |
| title_full | Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery |
| title_fullStr | Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery |
| title_full_unstemmed | Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery |
| title_short | Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery |
| title_sort | simultaneous classification of objects with unknown rejection scour using infra red sensor imagery |
| topic | infra-red ATR target classification unknown rejection OOD open-set recognition |
| url | https://www.mdpi.com/1424-8220/25/2/492 |
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