Model-based classification of radar images

Abstract

Abstract-A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Bayesian classification, including the determination of a correspondence between unordered random features, is shown to be tractable, yielding a classification algorithm, a method for estimating error rates, and a tool for evaluating performance sensitivity. The radar image features used for classification are point locations with an associated vector of physical attributes; the attributed features are adopted from a parametric model of high-frequency radar scattering. With the emergence of wideband sensor technology, these physical features expand interpretation of radar imagery to access the frequencyand aspect-dependent scattering information carried in the image phase.Index Terms-Model-based classification, parametric modeling, point correspondence, radar image analysis.

Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR

“…ASCs are local descriptors with rich, physically relevant information. It is demonstrated that ASCs can be handle various EOCs with good performances [20][21][22][23][24]. For the test samples, which cannot be reliably classified by CNN, they are possibly from EOCs.…”

“…The scattering center features reflect the electromagnetic scattering characteristics of the target such as attributed scattering centers (ASCs) [18,19]. ASCs describe the local structures of the target by several physically relevant parameters, which have been demonstrated notably effectively for SAR ATR especially under the extended operating conditions (EOCs) [19][20][21][22][23][24][25]. In [21], an ASC-matching method is proposed based on Bayesian theory with application to target recognition.…”

“…ASCs describe the local structures of the target by several physically relevant parameters, which have been demonstrated notably effectively for SAR ATR especially under the extended operating conditions (EOCs) [19][20][21][22][23][24][25]. In [21], an ASC-matching method is proposed based on Bayesian theory with application to target recognition. Ding et al propose several ways to apply ASCs to SAR ATR, e.g., one-to-one ASC matching [22][23][24] and ASC-based target reconstruction [25].…”

“…Specially, for the features without unified forms, e.g., the unordered scattering centers, a similarity or distance measure is often first defined for these features. Afterwards, the target type is determined based on the maximum similarity or minimum distance [19][20][21][22][23][24].…”

SAR ATR of Ground Vehicles Based on ESENet

“…Additionally, such methods suffer from severe overfitting [5]. Model-based methods were proposed to solve the above problem [6,7]. In the model-based methods, SAR images are predicted by computer-aided design model and the modeling procedure is usually complicated.…”