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  • Articles  (1,467)
  • 2015-2019  (1,467)
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  • Articles  (1,467)
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  • 11
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    A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2018-03-28
    Description: The lack of proper class discrimination among the hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this letter proposes an optimal geometry-aware transformation for enhancing the classification accuracy. The underlying idea of this method is to obtain a linear projection matrix by solving a nonlinear objective function based on the intrinsic geometrical structure of the data. The objective function is constructed to quantify the discrimination between the points from dissimilar classes on the projected data space. Then, the obtained projection matrix is used to linearly map the data to more discriminative space. The effectiveness of the proposed transformation is illustrated with three benchmark real-world HS data sets. The experiments reveal that the classification and dimensionality reduction methods on the projected discriminative space outperform their counterpart in the original space.
    Print ISSN: 1545-598X
    Electronic ISSN: 1558-0571
    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 12
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    Nonrigid Image Registration for Low-Altitude SUAV Images With Large Viewpoint Changes (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Low-altitude aerial photography using small unmanned aerial vehicles (SUAVs) with large viewpoint changes causes nonrigid distortions and low overlap ratios. We present a nonrigid feature-based low-altitude SUAV image-registration method. The key idea of our method is to maintain a high matching ratio on inliers while taking advantage of outliers for varying the warping grids. Thus, accurate image transformation over the overlapping areas as well as a good approximation of the real transformation over the nonoverlapping areas can be obtained. Experiments on feature matching and image registration are performed using 42 pairs of SUAV images. Our method exhibited a favorable performance as compared with four state-of-the-art methods, even with up to 80% outliers.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 13
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    Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: In this letter, the recently developed extreme gradient boosting (Xgboost) classifier is implemented in a very high resolution (VHR) object-based urban land use–land cover application. In detail, we investigated the sensitivity of Xgboost to various sample sizes, as well as to feature selection (FS) by applying a standard technique, correlation-based FS. We compared Xgboost with benchmark classifiers such as random forest (RF) and support vector machines (SVMs). The methods are applied to VHR imagery of two sub-Saharan cities of Dakar and Ouagadougou and the village of Vaihingen, Germany. The results demonstrate that Xgboost parameterized with a Bayesian procedure, systematically outperformed RF and SVM, mainly in larger sample sizes.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 14
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    Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Water-body segmentation is an important issue in remote sensing and image interpretation. Classic methods for counteracting this problem usually include the construction of index features by combining different spectra, however, these methods are essentially rule-based and fail to take advantage of context information. Additionally, as the quality of image resolution improves, these methods are proved to be inadequate. With the rise of convolutional neural networks (CNN), the level of research about segmentation has taken a huge leap, but the field is still facing an increasing demand for data and the problem of blurring boundaries. In this letter, a new segmentation network called restricted receptive field deconvolution network (RRF DeconvNet) is proposed, with which to extract water bodies from high-resolution remote sensing images. Compared with natural images, remote sensing images have a weaker pixel neighborhood relativity; in consideration of this challenge, an RRF DeconvNet compresses the redundant layers in the original DeconvNet and no longer relies on a pretrained model. In addition, to tackle the blurring boundaries that occur in CNN, a new loss function called edges weighting loss is proposed to train segmentation networks, which has been shown to significantly sharpen the segmentation boundaries in results. Experiments, based on Google Earth images for water-body segmentation, are presented in this letter to prove our method.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 15
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    Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2018-03-28
    Description: Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called hyperspectral feature adaptation and augmentation (HFAA) for cross-scene HSI classification. The proposed HFAA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. To further enhance the common subspace representation, we propose to augment it by the feature selection strategy. HFAA can make full use of the original features from both source and target domains, and increase the similarity of the samples with the same label from the two domains. Our proposed HFAA method achieves compact but discriminative feature representations, which make it well suited for data sets with a large number of classes and huge interclass ambiguity. Experimental results on the Earth Observing 1 hyperspectral data set show that HFAA can produce state-of-the-art performance and surpass previous methods.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 16
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    Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 17
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    IEEE Geoscience and Remote Sensing Letters information for authors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 18
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    PolSAR Image Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Polarimetric synthetic aperture radar (PolSAR) image classification is an important application. Advanced deep learning techniques represented by deep convolutional neural network (CNN) have been utilized to enhance the classification performance. One current challenge is how to adapt deep CNN classifier for PolSAR classification with limited training samples, while keeping good generalization performance. This letter attempts to contribute to this problem. The core idea is to incorporate expert knowledge of target scattering mechanism interpretation and polarimetric feature mining to assist deep CNN classifier training and improve the final classification performance. A polarimetric-feature-driven deep CNN classification scheme is established. Both classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain are used to drive the proposed deep CNN model. Comparison studies validate the efficiency and superiority of the proposal. For the benchmark AIRSAR data, the proposed method achieves the state-of-the-art classification accuracy. Meanwhile, the convergence speed from the proposed polarimetric-feature-driven CNN approach is about 2.3 times faster than the normal CNN method. For multitemporal UAVSAR data sets, the proposed scheme achieves comparably high classification accuracy as the normal CNN method for train-used temporal data, while for train-not-used data it obtains an average of 4.86% higher overall accuracy than the normal CNN method. Furthermore, the proposed strategy can also produce very promising classification accuracy even with very limited training samples.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 19
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    Frequency Domain Inverse Profiling of Buried Dielectric Elliptical-Cylindrical Objects Using Evolutionary Programming (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2018-03-28
    Description: An efficient method based on the evolutionary programming (EP) technique is proposed for inverse profiling of 2-D buried dielectric objects with elliptical cross sections. In particular, EP with Cauchy mutation operator (EP-CMO), as its first reported implementation to inverse problems, is utilized as a stochastic optimization tool for quantitatively reconstructing buried objects. Moreover, the method of moments technique in conjunction with conjugate gradient-fast Fourier transform method is used, as a fast and simple frequency domain forward solver, in each iteration of the proposed method. Numerical results for different case studies are presented and analyzed. To assess the proposed EP-CMO method, the results are also compared statistically with that of three other well-known optimization techniques, namely, EP with Gaussian mutation, particle swarm optimization, and genetic algorithms. The results reveal that EP-CMO is a significantly more robust and efficient optimization tool in reconstruction of this class of buried objects.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 20
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    Knowledge-Aided Covariance Matrix Estimation via Kronecker Product Expansions for Airborne STAP (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: This letter proposes a new approach for knowledge-aided estimation of structured clutter covariance matrices (CCMs) in airborne radar systems with limited training data. First, we model the CCM in space–time adaptive processing (STAP) as a sum of low-rank Kronecker products. We then apply a permutation operation to convert the Kronecker factors into linear structures and propose a novel CCM estimation method under the maximum-likelihood framework. Employing a proximal gradient algorithm, the proposed method simultaneously exploits the knowledge about the clutter and the Kronecker structure of the CCM. We finally evaluate the performance of the proposed method using real data from airborne STAP.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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