136 research outputs found

    Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

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    A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses

    A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

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    In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

    Interpretation of images from intensity, texture and geometry

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    Guided data augmentation for improved semi-supervised image classification in low data regime.

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    Deep learning models have achieved state of the art performances, especially for computer vision applications. Much of the recent successes can be attributed to the existence of large, high quality, labeled datasets. However, in many real-world applications, collecting similar datasets is often cumbersome and time consuming. For instance, developing robust automatic target recognition models from infrared images still faces major challenges. This is mainly due to the difficulty of acquiring high resolution inputs, sensitivity to the thermal sensors\u27 calibration, meteorological conditions, targets\u27 scale and viewpoint invariance. Ideally, a good training set should contain enough variations within each class for the model to learn the most optimal decision boundaries. However, when there are under-represented regions in the training feature space, especially in low data regime or in presence of low-quality inputs, the model risks learning sub-optimal decision boundaries, resulting in sub-optimal predictions. This dissertation presents novel data augmentation (DA) strategies aimed at improving the performance of machine learning models in low data regimes. The proposed techniques are designed to augment limited labeled datasets, providing the models with additional information to learn from.\\ The first contribution of this work is the development of Confidence-Guided Generative Augmentation (CGG-DA), a technique that trains and learns a generative model, such as Variational Autoencoder (VAE) and Deep Convolutional Generative Adversarial Networks (DCGAN), to generate synthetic augmentations. These generative models can generate labeled and/or unlabeled data by drawing from the same distribution as the under-performing samples based on a baseline reference model. By augmenting the training dataset with these synthetic images, CGG-DA aims to bridge the performance gap across different regions of the training feature space. We also introduce a Tool-Supported Contextual Augmentation (TSC-DA) technique that leverages existing ML models, such as classifiers or object detectors, to label available unlabeled data. Samples with consistent and high confidence predictions are used as labeled augmentations. On the other hand, samples with low confidence predictions might still contain some information even though they are more likely to be noisy and inconsistent. Hence, we keep them and use them as unlabeled samples during. Our third proposed DA explores the use of existing ML tools and external image repositories for data augmentation. This approach, called Guided External Data Augmentation (EG-DA), leverages external image repositories to augment the available dataset. External repositories are typically noisy, and might include a lot of out-of-distribution (OOD) samples. If included in the training process without proper handling, OOD samples can confuse the model and degrade the performance. To tackle this issue, we design and train a VAE-based anomaly detection component and use it to filter out any OOD samples. Since our DA includes both labeled data and a larger set of unlabeled data, we use semi-supervised training to exploit the information contained in the generated augmentations. This can guide the network to learn complex representations, and generalize to new data. The proposed data augmentation techniques are evaluated on two computer vision applications, and using multiple scenarios. We also compare our approach, using benchmark datasets, to baseline models trained on the initial labeled data only, and to existing data augmentation techniques. We show that each proposed augmentation consistently improve the results. We also perform an in-depth analysis to justify the observed improvements

    Fast Model-based Automatic Target Recognition Method for Synthetic Aperture Sonar Image

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    针对基于合成孔径声呐(SAS)图像目标识别的先验模板获取困难、运算复杂度高的问题,该文提出一种基于模型的改进型相关快速识别方法。首先,基于构造凸壳估计目标姿态角,实现目标成像几何关系的估计;其次,提出改进的基于隐藏点移除的目标图像快速生成方法,可实时得到各备选目标对应成像几何关系的仿真图像;进而基于图像相关实现目标图像识别;最后,仿真实验证明了算法的有效性。仿真实验结果表明,相比于常规的直接模板识别方法,该方法识别率高,计算速度快。A modified model-based method is proposed to obtain sufficient prior templates and reduce the computational complexity on Synthetic Aperture Sonar(SAS) automatic target recognition.First, a quick method based on build convex hull is proposed to estimate the target pose quickly as well as the SAS imaging geometry for the specified target.Second, an improved method based on Hidden Point Removal(HPR) algorithm is proposed to obtain the target SAS simulation image effectively.Accordingly, the target recognition is realized by the correlation between the test image and the simulated image.Finally, the effectiveness of the proposed method is verified by the simulation experiment.It is shown that the proposed method can achieve higher computational efficiency than the conventional direct templet-based method, but remain the same high recognition rate.国家自然科学基金(61271391;41176032;41376040)资助课

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    Hybrid machine learning approaches for scene understanding: From segmentation and recognition to image parsing

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    We alleviate the problem of semantic scene understanding by studies on object segmentation/recognition and scene labeling methods respectively. We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation). On the other hand, under situations when target boundaries are not obviously observed and object shapes are not preferably detected, we explored some sparse representation classification (SRC) methods on ATR applications, and developed a fusion technique that combines the traditional SRC and a group constrained SRC algorithm regulated by a sparsity concentration index for improved classification accuracy on the Comanche dataset. Moreover, we present a compact rare class-oriented scene labeling framework (RCSL) with a global scene assisted rare class retrieval process, where the retrieved subset was expanded by choosing scene regulated rare class patches. A complementary rare class balanced CNN is learned to alleviate imbalanced data distribution problem at lower cost. A superpixels-based re-segmentation was implemented to produce more perceptually meaningful object boundaries. Quantitative results demonstrate the promising performances of proposed framework on both pixel and class accuracy for scene labeling on the SIFTflow dataset, especially for rare class objects

    Feature extraction and classification for hyperspectral remote sensing images

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    Recent advances in sensor technology have led to an increased availability of hyperspectral remote sensing data at very high both spectral and spatial resolutions. Many techniques are developed to explore the spectral information and the spatial information of these data. In particular, feature extraction (FE) aimed at reducing the dimensionality of hyperspectral data while keeping as much spectral information as possible is one of methods to preserve the spectral information, while morphological profile analysis is the most popular methods used to explore the spatial information. Hyperspectral sensors collect information as a set of images represented by hundreds of spectral bands. While offering much richer spectral information than regular RGB and multispectral images, the high dimensional hyperspectal data creates also a challenge for traditional spectral data processing techniques. Conventional classification methods perform poorly on hyperspectral data due to the curse of dimensionality (i.e. the Hughes phenomenon: for a limited number of training samples, the classification accuracy decreases as the dimension increases). Classification techniques in pattern recognition typically assume that there are enough training samples available to obtain reasonably accurate class descriptions in quantitative form. However, the assumption that enough training samples are available to accurately estimate the class description is frequently not satisfied for hyperspectral remote sensing data classification, because the cost of collecting ground-truth of observed data can be considerably difficult and expensive. In contrast, techniques making accurate estimation by using only small training samples can save time and cost considerably. The small sample size problem therefore becomes a very important issue for hyperspectral image classification. Very high-resolution remotely sensed images from urban areas have recently become available. The classification of such images is challenging because urban areas often comprise a large number of different surface materials, and consequently the heterogeneity of urban images is relatively high. Moreover, different information classes can be made up of spectrally similar surface materials. Therefore, it is important to combine spectral and spatial information to improve the classification accuracy. In particular, morphological profile analysis is one of the most popular methods to explore the spatial information of the high resolution remote sensing data. When using morphological profiles (MPs) to explore the spatial information for the classification of hyperspectral data, one should consider three important issues. Firstly, classical morphological openings and closings degrade the object boundaries and deform the object shapes, while the morphological profile by reconstruction leads to some unexpected and undesirable results (e.g. over-reconstruction). Secondly, the generated MPs produce high-dimensional data, which may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. Last but not least, linear features, which are used to construct MPs, lose too much spectral information when extracted from the original hyperspectral data. In order to overcome these problems and improve the classification results, we develop effective feature extraction algorithms and combine morphological features for the classification of hyperspectral remote sensing data. The contributions of this thesis are as follows. As the first contribution of this thesis, a novel semi-supervised local discriminant analysis (SELD) method is proposed for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (Local Linear Feature Extraction Methods (LLFE)) and supervised method (Linear Discriminant Analysis (LDA)) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Our second contribution is the application of morphological profiles with partial reconstruction to explore the spatial information in hyperspectral remote sensing data from the urban areas. Classical morphological openings and closings degrade the object boundaries and deform the object shapes. Morphological openings and closings by reconstruction can avoid this problem, but this process leads to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction, which means that object size is often incorrectly represented. Morphological profiles with partial reconstruction improve upon both classical MPs and MPs with reconstruction. The shapes of objects are better preserved than classical MPs and the size information is preserved better than in reconstruction MPs. A novel semi-supervised feature extraction framework for dimension reduction of generated morphological profiles is the third contribution of this thesis. The morphological profiles (MPs) with different structuring elements and a range of increasing sizes of morphological operators produce high-dimensional data. These high-dimensional data may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. To the best of our knowledge the use of semi-supervised feature extraction methods for the generated morphological profiles has not been investigated yet. The proposed generalized semi-supervised local discriminant analysis (GSELD) is an extension of SELD with a data-driven parameter. In our fourth contribution, we propose a fast iterative kernel principal component analysis (FIKPCA) to extract features from hyperspectral images. In many applications, linear FE methods, which depend on linear projection, can result in loss of nonlinear properties of the original data after reduction of dimensionality. Traditional nonlinear methods will cause some problems on storage resources and computational load. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which estimates the eigenvectors through iteration. Without performing eigen decomposition on the Gram matrix, our approach can reduce the space complexity and time complexity greatly. Our last contribution constructs MPs with partial reconstruction on nonlinear features. Traditional linear features, on which the morphological profiles usually are built, lose too much spectral information. Nonlinear features are more suitable to describe higher order complex and nonlinear distributions. In particular, kernel principal components are among the nonlinear features we used to built MPs with partial reconstruction, which led to significant improvement in terms of classification accuracies. The experimental analysis performed with the novel techniques developed in this thesis demonstrates an improvement in terms of accuracies in different fields of application when compared to other state of the art methods

    Geometristen muotojen reaaliaikainen tunnistus

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    Kynä- ja kosketuskäyttöliittymät vaativat toimiakseen tehokasta ja tarkkaa hahmontunnistusta. Tässä työssä esitellään reaaliaikaisen hahmontunnistuksen käsitteistöä, yleisiä menetelmiä ja aikaisempaa tutkimusta. Lyhyesti käsitellään eri tutkimusryhmien esittämiä hahmontunnistusjärjestelmiä. Lisäksi esitellään geometrisiin piirteisiin perustuva hahmontunnistusjärjestelmä. Työ antaa yksityiskohtaiset kuvaukset piirtoviivan esiprosessointi- ja piirteenirrotusalgoritmeista sekä hahmoluokittelumenetelmästä. Lisäksi kuvaillaan hahmontunnistusheuristiikka kahdelle yksinkertaiselle muodolle (nuoli ja tähti). Joukko koehenkilöitä käytti työssä toteutettua graa_sta käyttöliittymää, minkä tuloksena saatiin realistiset tulokset järjestelmän laskennallisesta suorituskyvystä ja tarkkuudesta: toteutettu järjestelmä on laskennallisesti nopea mutta tunnistustarkkuus monitulkintainen. Lopuksi pohditaan valitun lähestymistavan ongelmia ja rajoitteita.Effective sketch recognition is the basis for pen and touch-based human-computer interfaces. In this thesis the concepts, common methods and earlier work in the research area of online symbol recognition are presented. A set of shape recognition approaches proposed in the past by various research teams are briefly introduced. An online shape recognizer using global geometric features is described. The preprocessing and feature extraction algorithms as well as the shape classification method are described in detail. Recognition heuristics for two simple shapes (arrow and star) are suggested. A graphical user interface was implemented and a group of subjects employed to obtain realistic results of the computational performance and recognition accuracy of the system: the implemented system performs fast but the results on the recognition accuracy were ambiguous. Finally, the problems and restrictions of the approach are discussed
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