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An algorithm to simulate the reflectance of clouds using visible bands was developed based on the simplified radiative transfer equation and two assumptions. The assumptions and algorithm were evaluated using a Landsat 8 sub-image of 015/035 (path/row) acquired on 27 August 2013. The relationship of visible bands in clear regions was verified to be linear. The effects of clouds in visible bands were...
In this paper the results of multi-frequency (at 5.6GHz, 15GHz and 37GHZ) and polarization measurements of clear and cloudy sky apparent temperatures are presented, measured under various observation angles. The results have been obtained during the measurements carried out in Armenia from the measuring complex built under the framework of ISTC Projects A-872 and A-1524. The measurements were carried...
Surface based millimeter wave radar systems play a substantial role in remote sensing of clouds. A preliminary analysis of the results obtained from the algorithm developed for cloud classification is presented. Our aim is to classify different cloud types (drizzling, precipitating, Mixed Phase, Ice clouds and non-meteorological targets ) solely based on Ka-band radar data. A fuzzy logic technique...
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...
The daily precipitation datasets of the Qinghai-Tibetan plateau (QTP) are mainly assimilated from remote sensing products and in-situ observations. The accuracy of those datasets needs further improvement with environmental and meteorological factors. This paper selected the related environmental and meteorological factors as input; k-Nearest Neighbor (KNN), Multivariate Adaptive Regression Splines...
A probabilistic attenuation correction technique for differential reflectivity ZDR, based on the Bayesian theory, in a dual polarization networked environment is proposed. The proposed technique assumes a proportional relationship between specific differential attenuation ADP and specific differential phase KDP, and a power law relationship between backscattering differential phase δco and ZDR. The...
The goal of this study was compare hyperspectral and multispectral imagery for mapping broad land-cover classes at the spatial scale of a satellite image. The study area was the San Francisco Bay Area and was roughly the size of a Landsat scene (30,000 km2). The Random Forests machine learning and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were compared to predictor variables...
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...
In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the...
This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be...
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...
A novel background dictionary learning and structured sparse representation based anomaly detection method is proposed for hyperspectral imagery. First, a robust PCA spectrum dictionary is learned from the plausible background area detected by the local RX detector. With the learned dictionary, the reweighted Laplace prior based structured sparse representation model is then employed to reconstruct...
This paper presents a new algorithm to directly extract 3D road boundaries from mobile laser scanning (MLS) point clouds. The algorithm includes two stages: 1) non-ground point removal by a voxel-based elevation filter, and 2) 3D road surface extraction by curb-line detection based on energy minimization and graph cuts. The proposed algorithm was tested on a dataset acquired by a RIEGL VMX-450 MLS...
The distorted Born approximation (DBA) combined with the numerical solutions of Maxwell equations (NMM3D) has been used for the radar backscattering model for NASA's Soil Moisture Active Passive (SMAP) mission. The models for vegetated surfaces such as wheat, grass, soybean and corn have been validated with the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) data. In this paper...
In this paper, we propose a learning-based road network extraction scheme from high resolution satellite. First, the convolutional neural network(CNN), which is able to capture large context of local structures, are applied to predict the probability of a pixel belonging to road regions, and assign labels to each pixel to describe whether it is road. Then, a line integral convolution based algorithm...
Imaging period is an important consideration to geostationary interferometric microwave sounder (GIMS) when mapping fast changing target such as typhoon. GIMS simulation system with near real case observation target can evaluate system performance in different system configurations and thus help determine the optimal imaging period. In this paper, GIMS simulation system using MATLAB and near real...
The rich spectral information in hyperspectral imagery gives rise to huge storage and transmission costs. Dimensionality reduction aims to reduce the space complexity in hyperspectral imagery by projecting data into a low-dimensional subspace. There has been an increasing interest in dimensionality reduction driven by random projections due to its data-independent representation as well as desirable...
Based on sparsity or compressibility of microwave radiation images, Compressive Sensing in this paper is adopted to achieve microwave radiation imaging in order to reduce the complexity and hardware cost of the imaging system and get the image of high spatial resolution. Compared with the common wavelet basis, differential matrix is proposed to sparsely represent microwave radiation images. OMP algorithm...
In this paper, a sub-pixel mapping (SPM) method based on super-resolution then spectral unmixing (SRTSUSPM) is proposed. In the proposed framework, firstly projection onto convex set (POCS) model with the endmembers of interest is applied to original imagery to obtain a high-resolution imagery; then the fraction images are derived from the high-resolution imagery by linear spectral mixture analysis...
Built-up areas are typical man-made structure in urban environment, and timely and accurately acquiring built-up area layers can provide necessary geo-spatial information for planners and policymakers. In this paper, a data field-based method is proposed for the automated detection of built-up areas from high-resolution satellite images. This method views the local corner features of buildings as...
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