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This paper proposes the kernel entropy component analysis (KECA) for clustering remote sensing data. The method generates nonlinear features that reveal structure related to the Rényi entropy of the input space data set. Unlike other kernel feature extraction methods, the top eigenvalues and eigenvectors of the kernel matrix are not necessarily chosen. Data are interestingly mapped with a distinct...
Forest biomass is an important indicator in carbon sequestration capacity and forest carbon budget evaluation, but there is less focus on shrub biomass. Multi-angle images provide the volume scattering information which could improve inversion accuracy. HJ-1 satellites are the sun synchronous recurrent frozen orbit small satellite constellation for environment and disaster monitoring and forecasting...
Many studies [1]–[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of pattern recognition and machine learning, many researches [3]–[5] show that kernel method is computationally efficient,...
Efficient estimation of the interferometric phase and complex correlation is fundamental for the full exploitation of SAR Interferometry capabilities [1]. Particularly when combining interferometric measures arising both from distributed and concentrated point targets, the interferometric phase has to be correctly extracted in order to preserve its physical meaning and respect the homogeneity hypothesis...
The classification of multisensor data sets, consisting of multitemporal SAR data and multispectral is addressed. In the present study, Import Vector Machines (IVM) are applied on two data sets, consisting of (i) Envisat ASAR/ERS-2 SAR data and a Landsat 5 TM scene, and (ii) TerraSAR-X data and a RapidEye scene. The performance of IVM for classifying multisensor data is evaluated and the method is...
In this paper we propose an unsupervised approach to change detection by computing the difference image directly in the feature spaces. The resulting difference kernel, that is a combination of kernels computed on the coregistered and radio-metrically matched input images, is used to train a nonlinear partitioning algorithm. In order to apply the kernel k-means, issues related to the initialization...
Map projection is a key task in cartography that transforms the geographical coordinates from one coordinate system to another. It has been widely used in the Geographic Information System application. However, map projection is a very time-consuming task, and fast processing speed is often required in interactive GIS scenarios. Parallel computation provides an opportunity to reduce run times. Nowadays,...
In this work we present an adaptation algorithm focused on the description of the measurement changes under different acquisition conditions. The adaptation is carried out by transforming the manifold in the first observation conditions into the corresponding manifold in the second. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping...
In this work, we face the problem of training sample collection for the estimation of biophysical parameters by adopting the active learning approach. In particular, we propose two active learning strategies specifically developed for Gaussian Process (GP) regression. The first one is based on adding samples that are distant from the current training samples in the kernel space while the second one...
Extracting man-made objects in satellite images which are generated from the meter to sub-meter resolution plays an important role in remote satellite image analysis. However, spectral characteristics of urban land objects are so similar. So the classification accuracies are far from satisfactory by using only spectral information. As a result, researchers turn to incorporate geometrical information...
This paper proposes the use of kernel ridge regression (KRR) to derive surface and atmospheric properties from hyperspectral infrared sounding spectra. We focus on the retrieval of temperature and humidity atmospheric profiles from Infrared Atmospheric Sounding Interferometer (MetOp-IASI) data, and provide confidence maps on the predictions. In addition, we propose a scheme for the identification...
Prior knowledge can significantly improve the retrieval of surface spectral albedo from satellite observations. This paper compares two methods that derive HJ-1 surface albedo in Heihe region by using prior knowledge based on kernel-driven BRDF model, with that derived by assuming Lambertian surface. The first algorithm (algorithm I) uses the backup algorithm of operational MODIS BRDF/Albedo product;...
Purpose of this work is the study of cloud detection techniques. This work identifies the cloud cover of optical images acquired by the QuickBird satellite, comparing these with others of the same area, acquired by Landsat 7 in which there are no clouds. The images are combined using an early fusion technique [1]. The tool exploits the neighborhood model [2] for increasing the amount of information...
The goal of this study is the discrimination of seven tree species. As a well known approach the k-nearest neighbor classifier is compared to a support vector machine based decision tree. This classifier uses advanced support vector machines to implement a hierarchical classification scheme by combining it with decision tree induction. At each node of the decision tree a support vector machine is...
In this paper, a Generalized Kernel-based Ensemble Learning (GKEL) algorithm for hyperspectral classification problems is presented. The proposed algorithm generalizes the Sparse Kernel-based Ensemble Learning (SKEL) technique, developed previously by the authors. SKEL optimally and sparsely weights and aggregates an ensemble of individual SVM classifiers which independently conduct learning within...
Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (AMBRALS) has been developed for scientific user community by Lucht et al. since 1995 as a surrogate for the operational MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo code. It is a family of kernel-driven BRDF models including the operational MODIS BRDF/Albedo main algorithm, and. adopts command line...
In this paper, we will presents our results in accelerating Radiative Transfer for TOVS (RTTOV) Radiative Transfer Model (RTM) scheme on many-core NVIDIA graphics processing units (GPUs). GPUs have evolved into a highly parallel, multi-threaded, many-core processors with tremendous computational speed and a high memory bandwidth. We will discuss how our GPU implementation is able to take advantage...
Nowcasting refers to short-term automated forecasting (0–6 hours or less) of high-impact weather events such as heavy rainfall that can produce severe flooding. Accurate and efficient nowcasting can be used to assist emergency managers in the decision-making process. This paper presents an evaluation of nowcasting performance within the Collaborative Adaptive Sensing of the Atmosphere (CASA) system...
High spatial resolution images are widely used in many different areas. In order to increase classification accuracy, texture feature are widely studied. This paper presents a method of object oriented texture analysis method based on Gabor filters. In fusion of spectral features and Gabor textures, composite kernel methods were applied. The results show its effectiveness in extracting texture information...
This paper presents a family of metrics for assessing image similarity. The methods use the Hilbert-Schmidt Independence Criterion (HSIC) to estimate nonlinear statistical dependence between multidimensional images. The proposed methods have very good theoretical and practical properties. We illustrate the performance in evaluating the quality of natural photographic images, hyperspectral images under...
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