This paper presents a fast SAR imagery algorithm for Ground Moving Target Imaging (GMTIm) based on the slope detection strategy combined with Time-Frequency Representation (TFR), which is known as Lv's Distribution (LVD). This fast imagery algorithm focuses on the solution of the ambiguity problems and relevant heavy computing load in SAR imagery. Firstly, according to the relationship between the slope of the range walk trajectory and the cross-track velocity of moving target, a new high-efficiency slope detection strategy based on gradient and level-line angle is presented in the image domain. Then, the Doppler centroid shift induced by cross-track velocity can also be obtained. Secondly, owing to the cross-track velocity estimated before, the Range Walk Migration Correction (RWMC) can be performed to concentrate the echo response of the moving target into a single range cell. Finally, due to the superior performance in representing multi-component Linear Frequency Modulation (LFM) signal, LVD is adopted here to represent the Doppler chirp rate of multiple moving targets in a Doppler Centroid Frequency and Chirp Rate domain (CFCR). The performance of the proposed algorithm is evaluated in terms of superiority and effectiveness using simulations, and the comparison between the proposed algorithm and the other conventional algorithms is also presented.
With the rapid development of the Internet of things and e-commerce, feature-based image retrieval and classification have become a serious challenge for shoppers searching websites for relevant product information. The last decade has witnessed great interest in research on content-based feature extraction techniques. Moreover, semantic attributes cannot fully express the rich image information. This paper designs and trains a deep convolutional neural network that the convolution kernel size and the order of network connection are based on the high efficiency of the filter capacity and coverage. To solve the problem of long training time and high resource share of deep convolutional neural network, this paper designed a shallow convolutional neural network to achieve the similar classification accuracy. The deep and shallow convolutional neural networks have data pre-processing, feature extraction and softmax classification. To evaluate the classification performance of the network, experiments were conducted using a public database Caltech256 and a homemade product image database containing 15 species of garment and 5 species of shoes on a total of 20,000 color images from shopping websites. Compared with the classification accuracy of combining content-based feature extraction techniques with traditional support vector machine techniques from 76.3% to 86.2%, the deep convolutional neural network obtains an impressive state-of-the-art classification accuracy of 92.1%, and the shallow convolutional neural network reached a classification accuracy of 90.6%. Moreover, the proposed convolutional neural networks can be integrated and implemented in other colour image database.
In this article, a novel imaging algorithm by combining two-step scaling transform (TSST) with structure-aided 2-D autofocus is proposed for the squint spotlight synthetic aperture radar (SAR). First, on the basis of planar wavefront assumption, a modified range-frequency linear scaling transform (MRFLST) and an azimuth-time nonlinear scaling transform (ATNST) are proposed to eliminate the coupling between range-frequency and azimuth-time of the received echo. Furthermore, to improve the efficiency, the MRFLST is implemented by using the principle of chirp scaling (PCS), which involves only complex multiplications and fast Fourier transforms (FFTs) without any interpolation, meanwhile, a constant scaling factor (CSF) selecting criteria is defined to avoid range spectrum aliasing. Then, to correct the phase error caused by the range measurement error and atmospheric propagation effects, the prior 2-D phase error structure implied in the TSST is analyzed. Finally, by integrating the derived 2-D phase error structure and range frequency fragmentation technique, a new 2-D autofocus algorithm is presented to improve the image quality. Simulated and real data experiments are carried out to verify the proposed algorithm. Index Terms-2-D autofocus, planar wavefront assumption, principle of chirp scaling (PCS), range frequency fragmentation, two-step scaling transform.
I. INTRODUCTIONB ENEFITING from the unique all-weather and all-time detecting ability, synthetic aperture radar (SAR) becomes the popular and extensively adopted means in supervising and imaging interesting areas [1]. The spotlight mode SAR can provide us with high resolution through continuously adjusting the antenna beam of the radar pointing to a preselected district in time of the echo acquisition [2].The coherence of data processing over the whole aperture synthesis period is the assurance for getting a well-focused imaging result and the imaging processes require precise measuring positions of the radar footprints. However, the ubiquitous Manuscript
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