Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

Automatic detection of multi-class objects in remote sensing images is a fundamental but challenging problem faced for remote sensing image analysis. Traditional methods are based on hand-crafted or shallow-learning-based features with limited representation power. Recently, deep learning algorithms, especially Faster region based convolutional neural networks (FRCN), has shown their much stronger detection power in computer vision field. However, several challenges limit the applications of FRCN in multi-class objects detection from remote sensing images: (1) Objects often appear at very different scales in remote sensing images, and FRCN with a fixed receptive field cannot match the scale variability of different objects; (2) Objects in large-scale remote sensing images are relatively small in size and densely peaked, and FRCN has poor localization performance with small objects; (3) Manual annotation is generally expensive and the available manual annotation of objects for training FRCN are not sufficient in number. To address these problems, this paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability. Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from several intermediate layers, whose receptive fields match different object scales, and an accurate object detection network (AODN) for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed objects to produce stronger response. For large-scale remote sensing images with limited manual annotations, we use cropped image blocks for training and augment them with re-scalings and rotations. The quantitative comparison results on the challenging NWPU VHR-10 data set, aircraft data set, Aerial-Vehicle data set and SAR-Ship data set show that our method is more accurate than existing algorithms and is effective for multi-modal remote sensing images.

Multi-scale object detection in remote sensing imagery with convolutional neural networks

http://hub.hku.hk/bitstream/10722/228807/1/content.pdf

Academic publishing and the myth of linguistic injustice

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.

https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-7/issue-1/071598/Target-detection-in-synthetic-aperture-radar-imagery--a-state/10.1117/1.JRS.7.071598.pdf

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Graduate writing is receiving increasing attention, particularly in contexts of diverse student bodies and widening access to universities. In many of these contexts, writing is seen as ‘a problem’ in need of fixing. Often, the problem and the solution are perceived as being solely located in notions of deficit in individuals and not in the broader embedded and sometimes invisible discourse practices. An academic literacies approach shifts the focus from the individual to broader social practices. This research project emerged out of an attempt to develop a graduate research-writing pedagogy from an academic literacies perspective. We present a detailed case study of one Masters' student to illustrate the results of a pedagogy that moved beyond notions of deficit and support. We argue that to be successful research writers, students need to (1) become discourse analysts; (2) develop authorial voice and identity; and (3) acquire critical competence.

Beyond Deficit: Graduate Student Research-Writing Pedagogies.

This paper presents an implementation of the discrete wavelet transform (DWT) using passive LC filters for operating a dynamic voltage restorer (DVR) system. The proposed implementation is based on designing Butterworth passive LC filters with cutoff frequencies that are identical to cutoff frequencies of DWT associated digital filters. These passive LC filters can detect abnormal conditions that may disrupt the quality of the power supplied to sensitive loads in a power system. Detecting any abnormal condition is realized through extracting high- and low-frequency components present in system voltages using high-pass and low-pass filters, respectively. The designed Butterworth passive LC passive filters are third-order systems to simplify their practical implementation as well as their integration with the test power system and the DVR. Simulation and experimental test results for transient voltage dip and steady-state harmonic distortion cases show significant performance improvement of the DVR system operated by the designed Butterworth passive LC filters. The proposed DWT-operated DVR system using Butterworth passive LC filters is implemented and tested for improving the power quality under different abnormal conditions.

Cecilia Moloney

Papers

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Beyond Deficit: Graduate Student Research-Writing Pedagogies.

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Implementation of a Dynamic Voltage Restorer System Based on Discrete Wavelet Transforms