Abstract:Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS). Sophisticated classification techniques can now be used in Sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC) which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for Sonar ATR that ret… Show more
“…We answer these questions by demonstrating, for the first time, an efficient and flexible classification algorithm that utilizes the in-scene MSAR-derived motion information. This method complements existing approaches to classification [11][12][13][14][15][16][17][18][19] and image segmentation [20][21][22][23][24][25][26][27] that exploit the spatial structure of static amplitude images. Our experimental results, performed on imagery captured by the U.S.…”
We present novel experimental evidence that demonstrates the effectiveness of exploiting scene motion information for the analysis of scene structure in maritime imaging applications. We analyze data captured by our novel airborne Multi-channel SAR (MSAR) system that is particularly suited to sampling the velocity profile of scatterers in the maritime environment. While previous works have shown the utility MSAR systems for correcting scene motion induced blurring artifacts, our work shows, for the first time, how the information furnished by an MSAR system can systematically render accurate classification of maritime scenes into different perceptual categories. We offer a methodology that is superior to traditional classification techniques that are based purely on the spatial structure of an image. Furthermore, the simplicity of the feature space involved together with the demonstrated classification performance on imagery captured by our airborne MSAR system underscore the strength of the methodology. INDEX TERMS Multi-channel synthetic aperture radar (SAR), ocean imaging, image classification
“…We answer these questions by demonstrating, for the first time, an efficient and flexible classification algorithm that utilizes the in-scene MSAR-derived motion information. This method complements existing approaches to classification [11][12][13][14][15][16][17][18][19] and image segmentation [20][21][22][23][24][25][26][27] that exploit the spatial structure of static amplitude images. Our experimental results, performed on imagery captured by the U.S.…”
We present novel experimental evidence that demonstrates the effectiveness of exploiting scene motion information for the analysis of scene structure in maritime imaging applications. We analyze data captured by our novel airborne Multi-channel SAR (MSAR) system that is particularly suited to sampling the velocity profile of scatterers in the maritime environment. While previous works have shown the utility MSAR systems for correcting scene motion induced blurring artifacts, our work shows, for the first time, how the information furnished by an MSAR system can systematically render accurate classification of maritime scenes into different perceptual categories. We offer a methodology that is superior to traditional classification techniques that are based purely on the spatial structure of an image. Furthermore, the simplicity of the feature space involved together with the demonstrated classification performance on imagery captured by our airborne MSAR system underscore the strength of the methodology. INDEX TERMS Multi-channel synthetic aperture radar (SAR), ocean imaging, image classification
“…In particular, image classification is the process of organizing images into different classes based on the output of feature extraction operators applied to images. There are innumerable approaches to feature extraction, a necessary precursor to classification, including decision-theoretic approaches using quantitative descriptors such as length, area, and texture [ 1 , 2 ]; structural approaches using qualitative descriptors, such as relational descriptors [ 3 ]; projection of data into fixed basis sets, such as wavelets [ 4 ] and Zernike polynomial moments [ 5 ], or adaptive basis sets [ 6 ]. Other examples include robust edges and corners that are popular in computer vision, blind synthesis of template classes by using singular value decomposition, Karhunen–Loeve Transform [ 7 , 8 ] and estimation theoretic templates [ 9 ], motion-based covariance matrix-based features for multi-sensor architectures [ 10 ], and finally micro-Doppler- [ 11 ] and vibrometry-based [ 12 ] features that have applications in radar-based sensing systems.…”
The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.
“…The high resolution image is very useful for many applications, e.g. to find objects such as mines [4, 5] and wrecks [6] and to produce maps of the seafloor [7]. Synthetic aperture image formation [3] plays an important role in the multireceiver SAS system.…”
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