Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

Kroll, Christine; Werth, Monika von der; Leuck, H.; Stahl, Christoph; Schertler, Klaus

doi:10.1117/12.2262064

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…It exhibits the greatest possible distance (maximum margin) between the closest data points across different classes. Once the relevant support vectors (the closest data points to the margin) are contained in the training set, the optimization will always result in the same classifier, even with limited training data that is considerably small [52]. Thus, SVMs are much more robust to learn generalizable models from the small amount of training data as the one we use in this study.…”

Section: Vggnets Vs the Modified Vggnetsmentioning

confidence: 99%

Enhanced convolutional neural network for non-small cell lung cancer classification

Tashtoush

Obeidat²,

Al-Shorman³

et al. 2023

IJECE

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<p>Lung cancer is a common type of cancer that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy of the diagnosis relies highly on the doctor's expertise. Recently, clinical decision support systems based on deep learning valuable recommendations to doctors in their diagnoses. In this paper, we present several deep learning models to detect non-small cell lung cancer in CT images and differentiate its main subtypes namely adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. We adopted standard convolutional neural networks (CNN), visual geometry group-16 (VGG16), and VGG19. Besides, we introduce a variant of the CNN that is augmented with convolutional block attention modules (CBAM). CBAM aims to extract informative features by combining cross-channel and spatial information. We also propose variants of VGG16 and VGG19 that utilize a support vector machine (SVM) at the classification layer instead of SoftMax. We validated all models in this study through extensive experiments on a CT lung cancer dataset. Experimental results show that supplementing CNN with CBAM leads to consistent improvements over vanilla CNN. Results also show that the VGG variants that use the SVM classifier outperform the original VGGs by a significant margin.</p>

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Section: Vggnets Vs the Modified Vggnetsmentioning

confidence: 99%

Enhanced convolutional neural network for non-small cell lung cancer classification

Tashtoush

Obeidat²,

Al-Shorman³

et al. 2023

IJECE

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“…A hybrid system comprising Deep CNN (DCNN) and Support Vector Machines (SVM) algorithms has been designed at Airbus specifically for TerraSAR-X. This hybrid design has shown to decrease generalisation error producing complex non-linear constraints that give the highest possible differentiation between trained target classes and the surrounding environment, as well as reducing total throughput for ATR applications [17]. The hybrid system efficiently combines convolutional networks effective learning of invariant features, with support vector machines fast and powerful decision making to reduce the number of parameters and thus increases the runtime performance of the network Figure 8 shows the steps employed in the ATR processor.…”

Section: Terrasar-x Gcp Case Studymentioning

confidence: 99%

Development of Operational Applications for TerraSAR-X

et al. 2018

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In the course of the TerraSAR-X mission, various new applications based on X-Band Synthetic Aperture Radar (SAR) data have been developed and made available as operational products or services. In this article, we elaborate on proven characteristics of TerraSAR-X that are responsible for development of operational applications. This article is written from the perspective of a commercial data and service provider and the focus is on the following applications with high commercial relevance, and varying operational maturity levels: Surface Movement Monitoring (SMM), Ground Control Point (GCP) extraction and Automatic Target Recognition (ATR). Based on these applications, the article highlights the successful transition of innovative research into sustainable and operational use within various market segments. TerraSAR-X’s high orbit accuracy, its precise radar beam tracing, the high-resolution modes, and high-quality radiometric performance have proven to be the instrument’s advanced characteristics, through, which reliable ground control points and surface movement measurements are obtained. Moreover, TerraSAR-X high-resolution data has been widely exploited for the clarity of its target signatures in the fields of target intelligence and identification. TerraSAR-X’s multi temporal interferometry applications are non-invasive and are now fully standardised autonomous tools to measure surface deformation. In particular, multi-baseline interferometric techniques, such as Persistent Scatter Interferometry (PSI) and Small Baseline Subsets (SBAS) benefit from TerraSAR-X’s highly precise orbit information and phase stability. Similarly, the instrument’s precise orbit information is responsible for sub-metre accuracy of Ground Control Points (GCPs), which are essential inputs for orthorectification of remote sensing imagery, to locate targets, and to precisely georeference a variety of datasets. While geolocation accuracy is an essential ingredient in the intelligence field, high-resolution TerraSAR-X data, particularly in Staring SpotLight mode has been widely used in surveillance, security and reconnaissance applications in real-time and also by automatic or assisted target recognition software.

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Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

Cited by 2 publications

References 3 publications

Enhanced convolutional neural network for non-small cell lung cancer classification

Enhanced convolutional neural network for non-small cell lung cancer classification

Development of Operational Applications for TerraSAR-X

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