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This paper presents a unified Non-local Spectral-spatial Centralized Sparse Representation (NL-CSR) model for the hyper-spectral image classification. The proposed model integrates local sparsity and non-local mean centralized induced sparsity. To achieve rich spectral-spatial information, the centralized sparsity enforces the sparse coding vector towards its non-local structural self-similar mean...
Support vector machines is a very popular method in classification of hyperspectral images due to their good generalization capability even with a limited number of training datasets. However, the performance of SVM strongly depends on selection of kernel parameters when RBF kernel is used. In order to achieve a high classification performance, the kernel parameters, that are the value of regularization...
Efficient super-resolution of hyperspectral images (HSI) relies on the representational model (RM) that is capable of capturing the spatial and spectral correlation in hyperspectral images. In this paper, the spectral information in hyperspectral images is explained by linear spectral mixture model (LSMM), which expressed the observed pixels as a linear combination of endmembers, and the spatial information...
Continuous-wave (CW) electromagnetic induction (EMI) systems operating in the presence of magnetic soil often encounter issues with the voltage that the soil induces in the receive coil. Previously, an optimization procedure that represents the coils as stream functions and attempts to create coils to mitigate the effects of the soil was presented. In this paper, the optimization convergence is improved,...
The use of spatial information prior to spectral unmixing of hyperspectral data is a very active research line in recent years. There are many approximations that consider spatial characteristics of the data in order to guide the endmember identification/extraction procedure. In particular, the spatial preprocessing (SPP) algorithm can be used prior to most existing spectral-based endmember identification...
This paper investigates the deployment of multi-tethered InSAR system applied in GMTI missions. Since the baselines for GMTI are usually short, the tethers whose length is almost identical to the baselines are short as well. As a consequence, the deploy mechanism should not work under this condition to avoid the unsuccessful deployment caused by the output error of the mechanism. Hence, the free deployment...
The superpixels provided by an unsupervised segmentation algorithm are sets of neighboring pixels homogeneous in some sense. Therefore it is very likely that, in a classification problem, most pixels in a superpixel belong to the same class, namely if the homogeneity criterion is compatible with the class statistics. Superpixels are, therefore, a powerful device to express spatial contextual information...
With emphasis on physics-based techniques, a multi-objective optimization approach to combined radar-radiometer soil moisture estimation is presented in this work. Soil moisture estimation is demonstrated via application of this method to SMAP high resolution radar and coarse resolution radiometer data. Comparisons are then made with the SMAP baseline active-passive soil moisture output data product...
Soil moisture retrieval is important in microwave remote sensing, but its high dimensional and non-linearity make it difficult to realize. In this paper, an improved Fruit Fly Optimization Algorithm (IFOA) to optimize the Least Squares Support Vector Machine (LSSVM) Model is used for soil moisture retrieval. The sampling data for training and test is generated by using Advanced Integral Equation Model...
Remote sensing image reconstruction from sparsely observed data is eagerly demanded by the onboard imaging system to cut down data volume and maintain image quality. High-order Markov random fields describe the neighborhood constraints in the statistical form that could be integrated into compressed sensing to improve the reconstruction performance of remote sensing images. To this end, we built a...
This manuscript proposes a symmetric sparse representation (SSR) method to extract pure endmembers from Hyperspectral imagery (HSI). The SSR assumes that the desired endmembers and all the HSI pixels can be sparsely represented by each other and it formulates the endmember extraction problem into finding archetypes in the minimal convex hull of the HSI data. The optimization program of SSR is solved...
Synthetic Aperture Radar (SAR) remote sensing techniques play a significant role in modern agricultural crop monitoring by relating the plant structure (height, biomass, yield and growth-stage) to the backscattering behavior of the vegetative canopy. The current trend in crop monitoring is towards precision agriculture, which needs detailed morphology information. By predicting the physical structure,...
An extended GIHS (EGIHS) fusion approach is presented for merging the panchromatic (PAN) image and the multi-spectral (MS) image with model-based optimization (MBO). The pan-sharpened MS image is posed as the optimization solution of the proposed functional, which consists of two energy terms. The first energy term injects the details of the PAN image into the MS image with the generalized IHS (GIHS)...
Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency...
In this paper, a novel multiple-kernel learning (MKL) algorithm is proposed for classification of hyperspectral images. The goal of classification is to acquire the class label of each pixel. The land covers is linearly separable in the kernel space spanned by class labels (ideal kernel). The ideal kernel is used as the optimization objective of our proposed MKL algorithm. Linear programming (LP)...
This paper proposes a new supervised classification method for hyperspectral images combining the spectral and spatial information. The main contribution is presented by combining subspace-based support vector machine (SVMsub) and Markov random field (MRF). A SVM classifier integrated with a subspace projection is first used to model the posterior distributions of the classes from the spectral information...
Distributed passive radar imaging has been an emerging topic in radar imaging society because of its low-cost, increased-survivability and robustness. In the inverse problem of distributed passive imaging, location of receivers affects imaging quality a lot, while illuminators of opportunity remain to be uncontrollable. Therefore, we investigate the problem of receiver disposition optimization and...
In this paper, a fully automatic building reconstruction method for high resolution interferometric synthetic aperture radar (InSAR) data is presented. This method is based on stochastic geometrical model. Firstly, a building detection procedure is implemented on the big image and the entire scene is divided into building clips. After that, the reconstruction process is utilized for each building...
For one dimensional large aperture synthesis radiometers (ASRs), the low redundancy linear arrays (LRLAs) are usually the choice for the reason of the lowest system complexity. However, the number of LRLAs is very few, which makes the antenna arrangement of LRLAs inflexible. In this paper, the array configuration optimization for AFF-based nonuniform array is proposed. The AFF-based nonuniform array...
In this paper, we propose a fast PolSAR image superpixel segmentation method. This method takes a simple coarse-to-fine optimization technique to minimize a Markov-Random-Field (MRF) like energy function which integrates the Pol- SAR image statistic, spatial position and boundary smoothing. It updates boundary of superpixels staring with a large block level and iterates down to the final pixel level...
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