Synthetic aperture radar (SAR) image generation is crucial to SAR image interpretation when sufficient image samples are unavailable. Against this background, a method for SAR image generation of three-dimensional (3D) target is proposed in this paper. Specifically, this method contains three steps. Firstly, according to the system parameters, the echo signal in the two-dimensional (2D) time domain is generated, based on which 2D Fast Fourier Transform (2DFFT) is performed. Secondly, the hybrid moments (MoM)-large element physical optics (LEPO) method is used to calculate the scattering characteristics with the certain frequency points and incident angles according to the system parameters. Finally, range Doppler algorithm (RDA) is adopted to process the signal in the 2D-frequency domain with radar cross section (RCS) exported from electromagnetic calculations. These procedures combine RCS computations by FKEO solver and RDA to simulate raw echo signal and then generate SAR image samples for different squint angles and targets with reduced computational load, laying foundations for transmit waveform design, SAR image interpretation and other SAR related work.

In recent years, deep convolutional neural networks (CNN) have been widely used in synthetic aperture radar (SAR) image recognition. However, due to the difficulty in obtaining SAR image samples, training data is relatively few and overfitting is easy to occur when using traditional CNNS used in optical image recognition. In this paper, a CNN-based SAR image recognition algorithm is proposed, which can effectively reduce network parameters, avoid model overfitting and improve recognition accuracy. The algorithm first constructs a convolutional network feature extractor with a small size convolution kernel, then constructs a classifier based on the convolution layer, and designs a loss function based on distance measurement. The networks are trained in two stages: in the first stage, the distance measurement loss function is used to train the feature extraction network; in the second stage, cross-entropy is used to train the whole model. The public benchmark dataset MSTAR is used for experiments. Comparison experiments prove that the proposed method has higher accuracy than the state-of-the-art algorithms and the classical image recognition algorithms. The ablation experiment results prove the effectiveness of each part of the proposed algorithm.

Synthetic aperture radar (SAR) image classification is a popular yet challenging research topic in the field of SAR image interpretation. This paper presents a new classification method based on extreme learning machine (ELM) and the superpixel-guided composite kernels (SGCK). By introducing the generalized likelihood ratio (GLR) similarity, a modified simple linear iterative clustering (SLIC) algorithm is firstly developed to generate superpixel for SAR image. Instead of using a fixed-size region, the shape-adaptive superpixel is used to exploit the spatial information, which is effective to classify the pixels in the detailed and near-edge regions. Following the framework of composite kernels, the SGCK is constructed base on the spatial information and backscatter intensity information. Finally, the SGCK is incorporated an ELM classifier. Experimental results on both simulated SAR image and real SAR image demonstrate that the proposed framework is superior to some traditional classification methods.

Microwave imaging techniques, in particular, synthetic aperture radar (SAR), are promising tools for terrain surface measurement, irrespective of weather conditions. The coherent change detection (CCD) method is being widely applied to detect surface changes by comparing multiple complex SAR images captured from the same scanning orbit. However, in the case of a general damage assessment after a natural disaster such as an earthquake or mudslide, additional about surface change, such as surface height change, is strongly required. Given this background, the current study proposes a novel height change estimation method using a CCD model based on the Pauli decomposition of fully polarimetric SAR images. The notable feature of this method is that it can offer accurate height change beyond the assumed wavelength, by introducing the frequency band-divided approach, and so is significantly better than InSAR based approaches. Experiments in an anechoic chamber on a 1/100 scaled model of the X-band SAR system, show that our proposed method outputs more accurate height change estimates than a similar method that uses single polarimetric data, even if the height change amount is over the assumed wavelength.

Synthetic aperture radar (SAR) is an indispensable tool for low visibility ground surface measurement owing to its robustness against optically harsh environments such as adverse weather or darkness. As a leading-edge approach for SAR image processing, the coherent change detection (CCD) technique has been recently established; it detects a temporal change in the same region according to the phase interferometry of two complex SAR images. However, in the case of general damage assessment following an earthquake or mudslide, the technique requires not only the detection of surface change but also an assessment for height change quantity, such as occurs with a building collapse or road subsidence. While the interferometric SAR (InSAR) approach is suitable for height assessment, it is basically unable to detect change if only a single observation is made. To address this issue, we previously proposed a method of estimating height change according to phase interferometry of the coherence function obtained by dual band-divided SAR images. However, the accuracy of this method significantly degrades in noisy situations owing to the use of the phase difference. To resolve this problem, this paper proposes a novel height estimation method by exploiting the frequency characteristic of coherence phases obtained by each SAR image multiply band-divided. The results obtained from numerical simulations and experimental data demonstrate that our proposed method offers accurate height change estimation while avoiding degradation in the spatial resolution.

In this paper, we consider the optimization of measurement matrix in Compressed Sensing (CS) framework. Based on the boundary constraint, we propose a novel algorithm to make the “mutual coherence” approach a lower bound. This algorithm is implemented by using an iterative strategy. In each iteration, a neighborhood interval of the maximal off-diagonal entry in the Gram matrix is scaled down with the same shrinkage factor, and then a lower mutual coherence between the measurement matrix and sparsifying matrix is obtained. After many iterations, the magnitudes of most of off-diagonal entries approach the lower bound. The proposed optimization algorithm demonstrates better performance compared with other typical optimization methods, such as t-averaged mutual coherence. In addition, the effectiveness of CS can be used for the compression of complex synthetic aperture radar (SAR) image is verified, and experimental results using simulated data and real field data corroborate this claim.

Microwave imaging techniques, in particular synthetic aperture radar (SAR), are able to obtain useful images even in adverse weather or darkness, which makes them suitable for target position or feature estimation. However, typical SAR imagery is not informative for the operator, because it is synthesized using complex radio signals with greater than 1.0 m wavelength. To deal with the target identification issue for imaging radar, various automatic target recognition (ATR) techniques have been developed. One of the most promising ATR approaches is based on neural network classification. However, in the case of SAR images heavily contaminated by random or speckle noises, the classification accuracy is severely degraded because it only compares the outputs of neurons in the final layer. To overcome this problem, this paper proposes a self organized map (SOM) based ATR method, where the binary SAR image is classified using the unified distance matrix (U-matrix) metric given by the SOM. Our numerical analyses and experiments on 5 types of civilian airplanes, demonstrate that the proposed method remarkably enhances the classification accuracy, particular in lower S/N situations, and holds a significant robustness to the angular variations of the observation.

In this paper we present a new post enhancement method for single look complex (SLC) SAR imagery, which is based on phase-extension inverse filtering. To obtain a high-quality SAR image, the proposed method improves the mainlobe resolution as well as efficiently suppresses the sidelobes with low computational complexity. The proposed method extends the effective signal band up to the maximum-bandwidth allowed by a SAR system. The band-extension is achieved by adjusting the magnitude level of matched filtered SAR spectrum. Because the proposed method preserves the phase components of the spectrum unlike other super-resolution techniques and deconvolution techniques, it enhances a SAR image without causing any displacement. To verify the efficacy of the proposed method we apply it to a simulated SAR image and a real ERS-1 SAR image. The result images show that the proposed method improves the mainlobe resolution with low sidelobe levels.

This paper presents a method for land cover classification using the SIR-C/X-SAR imagery based on the maximum likelihood method and the polarimetric filtering. The main feature is to use polarimetric enhanced image information in the pre-processing stage for the classification of SAR imagery. First, polarimetric filtered images are created where a specific target is enhanced versus another, then the image data are incorporated into the feature vector which is essential for the maximum likelihood classification. Specific target classes within the SAR image are categorized according to the maximum likelihood method using the wavelet transform. Addition of polarimetric enhanced image in the preprocessing stage contributes to the increase of classification accuracy. It is shown that the use of polarimetric enhanced images serves efficient classifications of land cover.

The wavelet transform provides information both in the spatial domain and in the frequency domain because of its inherent nature of space-frequency analysis. This paper presents a classification result of synthetic aperture radar image obtained by JERS-1 based on the discrete wavelet transform. This paper points out that the wavelet analysis has yielded a fine result in texture classification compared to a conventional method with less computation time.