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This paper addresses the statistical segmentation of SAR (Synthetic Aperture Radar) image combining PM (Perona Malik) nonlinear diffusion model and MRF (Markov Random Field) model. First, the original SAR image is filtered using the modified PM nonlinear diffusion model, in which the diffusion coefficients along the tangent direction and the normal direction are approximated and simplified. Afterwards,...
Most cartographic work is made extracting features from aerial or space images. A first step in this work is segmenting the images in regions that represent, as close as possible, cartographic entities (e.g., roads, buildings, vegetation). Region-Growing segmentation is implemented in a multispectral image using an open source programming language. This segmentation method is analyzed for land used...
In this paper we address a prospective look at the problem of computational simulation for different distributed tasks related to analysis of multispectral data obtained with the use of remote sensing systems as required for end-user-oriented environmental monitoring, urban assessment/planning and natural resources management. This virtual processing software for analysis of multispectral remote sensing...
This paper proposes PRDC-SSIS, a new compressibility-feature based semantic-sensitive image segmentation method using PRDC. One of the drawbacks of traditional signal (pixel-color) based image segmentation is the poor capability to capture the semantical information contained in the images. Because the semantic information tends to be carried by a set of neighboring pixels, rather than an individual...
In this work, a novel method for segmentation of Remote Sensing (RS) images based on the Darwinian Particle Swarm Optimization (DPSO) for determining the n−1 optimal n-level threshold on a given image is proposed. The efficiency of the proposed method is compared with the Particle Swarm Optimization (PSO) based segmentation method. Results show that DPSO-based image segmentation performs better than...
The discrepancies caused by different cluster merging algorithms in fully polarimetric SAR classification are analyzed here. There are two often-used merging schemes, i.e., merging first to desirable cluster numbers and then iterative clustering and, the agglomerative hierarchical clustering, both using three different between-cluster distance measures herein. One sub-image of RadarSat-2 SAR SLC image...
This paper, we propose a novel over-segmentation method Feature Geometry Space Fusion (FGSF) for polarimetric SAR (POLSAR) data classification, which uses the polarimetric feature and geometric feature. In order to exam its performance, experiments on the data acquired by AIRSAR show that the over segment regions segmented by FGSF method performs better than meanshift when used for classification.
Recently, hyperspectral images are used to estimate the yield of food crops. The images consist of a large number of bands which requires sophisticated method for its analysis. One approach to reduce computational cost and to accelerate knowledge discovery is by eliminating bands that do not add value to the analysis. In this paper, a genetic algorithm based new sequence of principal component regression...
As one of the computer simulation models, RGM (Radiosity-Graphics combined Model) can take the processes of reflectance, transmittance and multiple scattering among and between canopies into account. It is appropriate for simulating the directional reflectance from some small canopies and the simulating results are validated well, but the model is difficult to simulate the reflectance of complex scenes,...
Contents of biochemical compounds such as chlorophyll, nitrogen, cellulose or lignin in foliage can be used as indicators of the actual tree physiological status and the tree previsible damage. Imaging spectroscopy has been often applied for estimation of relations between foliage spectral and biochemical properties of chlorophyll or nitrogen contents ([4], [10], [15]). But it can also be used for...
This paper proposed a new approach to estimate the abundance of each endmember at each pixel using distance geometry concepts and distance geometry constraints. It improves current hyperspectral unmixing algorithms in several aspects. Firstly, denoting the distance relationship with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of observation pixels, and the computation...
The multiple classifier system has received remarkable attentions for improving the performance of a single classifier in recent years. The random subspace method (RSM) is one of the multiple classifier systems. In RSM, classifiers are trained by data set with randomly selected and fix-sized feature subsets and are combined using simple majority vote in the final decision rule. The feature subset...
Spectral unmixing of hyperspectral remote sensing reflectance data is a challenging task, being the subject of many recent researches, and aims to retrieve both endmembers spectra and abundances. It turns out to be hardly feasible when the data are attenuated by additional mediums such as water column in the case of seabed estimation. In this case, a possible solution is to first correct the attenuation...
This paper proposes a method called Adjusted Linear Discriminant Analysis (ALDA) for feature extraction of hyperspectral remote sensing imagery. In this method the variances of the classes are considered as a weight to estimate between-class scattering matrix appropriately. Experimental results on well known hyperspectral dataset demonstrate that compared to conventional LDA based feature extraction...
Radial basis function (RBF) has been widely used in kernel-based approaches. This paper extended RBF kernels to weighted RBF (WRBF) kernels by introducing a weighting matrix A into RBF kernels. A key to success in implementing WRBF kernels is to design different appropriate weighting matrices to implement WRBF kernels. Three weighting matrices are of particular interest, covariance matrix, correlation...
In tropical regions, the seasonal phenology and the interannual variability of carbon fluxes remain poorly understood, and its representation in global vegetation models highly simplified. However, previous field studies have explored the temporal dynamics of Amazonian vegetation and have shown unexpected and significant seasonal pattern in this ecosystem. Moreover, as a major component of the global...
The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne mission concept focused on tropical cyclone (TC) inner core process studies. CYGNSS attempts to resolve the principle deficiencies with current TC intensity forecasts, which lies in inadequate observations and modeling of the inner core. CYGNSS consists of 8 GPS bistatic radar receivers deployed on separate nanosatellites. The...
The development of bi-continuous scattering model greatly enhanced the ability to model the snow scattering characteristics based on microstructure with most similarity to real snowpack. In this study, snow section images of snow microstructure were used to study the scattering characteristics of snow by using the reconstructed snow 3D microstructure. The equivalent grain size of the continuous random...
Vegetation indices are indicators for analyzing the properties of vegetation. The Normalized Difference Vegetation Index (NDVI) from optical remote sensing data is one of the most commonly used vegetation indices, which can exhibit the ecological characteristics of leafy materials, but lacks the ability to directly provide information on the woody materials. In this paper, we developed Microwave Vegetation...
ET plays an important role in the energy exchange mass transfer between the hydrosphere, atmosphere and biosphere. ET is a critical variable in hydrological modeling and provides important boundary conditions for weather prediction. Environment and Disasters Monitoring Microsatellite Constellation of A and B star (HJ-1A/B), which was developed by China, has high space and temporary resolution and...
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