&lt;title&gt;ATR subsystem performance measures using manual segmentation of SAR target chips&lt;/title&gt;

Power, Gregory J.; Weisenseel, Robert A.

doi:10.1117/12.357683

Cited by 9 publications

(4 citation statements)

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“…and other methods [15]- [17] have been proposed for segmentation of a SAR image. The reference [18] describes manually generating the segmentation of target and shadow regions as ground truth. Generally, for segmentation using CNN, supervised learning is performed using label images corresponding to input images, but the difficulty of the generation of label images for SAR ATR is a problem for applying this method.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

Furukawa

2018

Preprint

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The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for SAR ATR have been proposed, but most of them classify target classes from a target chip extracted from SAR imagery, as a classification for the third stage of SAR ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR imagery. The CNN named verification support network (VersNet) performs all three stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrary sizes with multiple classes and multiple targets, and outputs a SAR ATR image representing the position, class, and pose of each detected target. This report describes the evaluation results of VersNet which trained to output scores of all 12 classes: 10 target classes, a target front class, and a background class, for each pixel using the moving and stationary target acquisition and recognition (MSTAR) public dataset.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

Furukawa

2018

Preprint

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“…One manual seg-mentation method was proposed as a segmentation reference. It includes manual segmentation by an analyst followed by a quality control check by a supervisor [4]. However, the result of this segmentation is not publicly available and would be labour intensive to reproduce.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Segmentation with GMMs

Belloni

Aouf

Merlet

et al. 2017

International Conference on Radar Systems (Radar 2017)

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This paper proposes a new approach for Synthetic Aperture Radar (SAR) image segmentation. Segmenting SAR images can be challenging because of the blurry edges and the high speckle. The segmentation proposed is based on a machine learning technique. Gaussian Mixture Models (GMMs) were already used to segment images in the visual field and are here adapted to work with single channel SAR images. The segmentation suggested is designed to be a first step towards feature and model based classification. The recall rate is the most important as the goal is to retain most target's features. A high recall rate of 88%, higher than for other segmentation methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, was obtained. The next classification stage is thus not affected by a lack of information while its computation load drops. With this method, the inclusion of disruptive features in models of targets is limited, providing computationally lighter models and a speed up in further classification as the narrower segmented areas foster convergence of models and provide refined features to compare. This segmentation method is hence an asset to template, feature and model based classification methods. Besides this method, a comparison between variants of the GMMs segmentation and a classical segmentation is provided.

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“…The choice of segmentation algorithms is a key preliminary step to determining shape similarity. [5][6][7][8][9][10][11] All these factors of shape variability suggest that a certain level of uncertainty must be permitted between the stored template and the extracted object when constructing an automatic object recognition algorithm.…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed object recognition using multiple comparative measures

Power

2004

Image and Signal Processing for Remote Sensing IX

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In the real world of remote sensing, rarely does the extracted object precisely match a stored template of that object. A certain level of uncertainty must be permitted between the stored template and the extracted object. One solution to deal with this uncertainty is to evaluate measures between the object and template. Many measures have been introduced independently in literature for evaluating the statistical nature of the extracted objects such as a variety of shape and texture measures. The object is measured and compared to similar measures taken from the template. This paper suggests using measures extracted through a joint comparative process of template and object. It also suggests using multiple measures from the joint class of measures as opposed to using an individual measure to determine the sufficiency of the match. In particular, this paper demonstrates the value of using multiple comparative shape measures as opposed to one particular shape measure to achieve confidence in a match. The multiple-shape measure approach uses a matched filter measure, a Procrustes metric, a partial-direct hausdorf measure, and percent-pixels same measure. Each shape measure gives slightly different insight about the shape comparison which allows more confidence in the match. An experimental result is given that demonstrates the implementation and usefulness of the multiple comparative measure approach for recognizing objects from remotely sensed imagery.

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<title>ATR subsystem performance measures using manual segmentation of SAR target chips</title>

Cited by 9 publications

References 0 publications

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

SAR Image Segmentation with GMMs

Remotely sensed object recognition using multiple comparative measures

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