Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.

Object Detection in 20 Years: A Survey

Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy.

Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification

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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

Part I Introduction to LPI radar and waveform design: to see and not be seen LPI technology and applications ambiguity analysis of LPI waveforms FMCW radar phase shift keying techniques frequency shift keying techniques case study - anti-ship LPI missile seeker. Part II Intercept receiver strategies and signal processing: strategies for LPI radar interception Wigner distribution analysis of LPI waveforms LPI radar analysis using quadrature mirror filtering cyclostationary bi-frequency analysis of LPI radar waveforms. Appendix: MATLAB software.

Detecting and Classifying Low Probability of Intercept Radar

In this article, we present a novel method for high-resolution joint direction-of-arrivals (DOA) and multi-pitch estimation based on subspaces decomposed from a spatio-temporal data model. The resulting estimator is termed multi-channel harmonic MUSIC (MC-HMUSIC). It is capable of resolving sources under adverse conditions, unlike traditional methods, for example when multiple sources are impinging on the array from approximately the same angle or similar pitches. The effectiveness of the method is demonstrated on a simulated an-echoic array recordings with source signals from real recorded speech and clarinet. Furthermore, statistical evaluation with synthetic signals shows the increased robustness in DOA and fundamental frequency estimation, as compared with to a state-of-the-art reference method.

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Joint DOA and multi-pitch estimation based on subspace techniques

A new technique is developed for phase adjustment in ISAR imaging. The adjustment phase is found by iteratively solving an equation, which is derived by minimising the entropy of the image. This technique can be used to estimate adjustment phases of any form. Moreover, the optimisation method used in this technique is computationally more efficient than trial-and-error methods.

Minimum-entropy phase adjustment for ISAR

Polarimetric synthetic aperture radar (PolSAR) image classification, an important technique in the remote sensing area, has been deeply studied for a couple of decades. In order to develop a robust automatic or semiautomatic classification system for PolSAR images, two important problems should be addressed: 1) incorporation of spatial relations between pixels; 2) estimation of the number of classes in the image. Therefore, in this paper, we present a novel superpixel-based classification framework with an adaptive number of classes for PolSAR images. The approach is mainly composed of three operations. First, the PolSAR image is partitioned into superpixels, which are local, coherent regions and preserve most of the characteristics necessary for image information extraction. Then, the number of classes and each class center within the data are estimated using the pairwise dissimilarity information between superpixels, followed by the final classification operation. The proposed framework takes the spatial relations between pixels into consideration and makes good use of the inherent statistical characteristics and contour information of PolSAR data. The framework is capable of improving the classification accuracy, making the results more understandable and easier for further analyses, and providing robust performance under various numbers of classes. The performance of the proposed classification framework on one synthetic and three real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for unsupervised classification of PolSAR images.

Superpixel-Based Classification With an Adaptive Number of Classes for Polarimetric SAR Images

A new algorithm is presented for autofocus in synthetic aperture radar imaging. Entropy is used to measure the focus quality of the image, and better focus corresponds to smaller entropy. The phase response of the focus filter is modeled as a specially designed polynomial, and the coefficients of this polynomial are adjusted in sequence to minimize the entropy of the image. Because the order of this polynomial is adaptive, this algorithm applies more widely than the minimum-entropy algorithms with a fixed-order polynomial model

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SAR Minimum-Entropy Autofocus Using an Adaptive-Order Polynomial Model

An improved global range alignment is presented for inverse synthetic aperture radar (ISAR) imaging. The shifts of the echoes are modeled as a polynomial, and the coefficients of this polynomial are chosen to optimize a quality measure of range alignment. The shift in the time domain is carried out by introducing a phase ramp in the frequency domain in order to remove the limitation of integer steps. Because the quality measure of range alignment is calculated directly in the frequency domain, this method is computationally more efficient than the original global method.

Improved Global Range Alignment for ISAR

This paper presents a new approach for the squint-mode spotlight SAR imaging. Like the frequency scaling algorithm, this method starts with the received signal dechirped in range. According to the geometry for the squint mode, the reference range of the dechirping function is defined as the range between the scene center and the synthetic aperture center. In our work, the residual video phase is compensated firstly to facilitate the following processing. Then the range-cell migration with a high-order range-azimuth coupling form is processed by a nonlinear frequency scaling operation, which is different from the original frequency scaling one. Due to these improvements, the algorithm can be used to process high squint SAR data with a wide swath and a high resolution. In addition, some simulation results are given at the end of this paper to demonstrate the validity of the proposed method.

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Xingzhao Liu

Papers

Minimum-entropy phase adjustment for ISAR

Superpixel-Based Classification With an Adaptive Number of Classes for Polarimetric SAR Images

SAR Minimum-Entropy Autofocus Using an Adaptive-Order Polynomial Model

Improved Global Range Alignment for ISAR

Nonlinear frequency scaling algorithm for high squint spotlight SAR data processing