جستجوی مقالات مرتبط با کلیدواژه « مدل آمیخته گاوسی » در نشریات گروه « عمران »

Hyperspectral image contains hundreds of narrow and contiguous spectral bands. Because of this high spectral resolution, hyperspectral images provide valuable information from the earth surface materials and objects. By advances in remote sensing technology and production of hyper spectral data with high spatial and spectral information, using such data for a detailed study of the phenomenon is spreading quickly. One of the most important applications of hyperspectral data analysis is either supervised or unsupervised classification for land cover mapping. Among different unsupervised methods, Gaussian mixture model has attracted a lot of attention, due to its performance and efficient computational time. Gaussian Mixture Models (GMMs) have been frequently applied in hyperspectral image classification tasks. The problem of estimating the parameters in a Gaussian mixture model has been studied in the literature. Gibbs sampler is one of the methods that can be applied for this problem. Another method for estimation the parameters of a Gaussian mixture model is Expectation-Maximization (EM) algorithm. EM is a general method for optimizing likelihood functions and is useful in situations where data might be missing or simpler optimization methods fail .On the other hand, the large number of bands in a hyperspectral images leads into estimation of a large number of parameters. In the other point of view, the enormous amount of information provided by hyperspectral images increases the computational burden as well as the correlation among spectral bands. Thus, dimensionality reduction is often conducted as one of the most important steps before target detection to both maximize the detection performance and minimize the computational burden. In this paper, we use PCA and Random Projection (RP) for solving the high dimensionality of the data. In order to evaluate the proposed algorithm in real analysis scenarios, we used two benchmark hyperspectral data sets collected by AVIRIS and Reflective Optics System Spectrographic Imaging System (ROSIS). In order to evaluate the effectiveness of the proposed method which is based on the using GMMS and its parameter are estimated using Gibbs sampler method we used two well-known dataset ROSIS and AVIRIS hyperspectral images which they are acquired from a urban and agricultural area, respectively. Moreover, for better evaluation we used a simulated data which is attained using a toolbox which is known as HYDRA project. Investigations on the simulated dataset and two real hyperspectral data showed that the case in which the number of bands has been reduced in the pre-processing stage using either RP or PCA in the feature space, can result the highest accuracy and efficiency for thematic mapping. We also demonstrated that the superiority of the Gibbs sampler in comparison with EM algorithm for estimating the GMM parameters. For instance, in Pavia university dataset, the overall accuracy and Kappa coefficient was 88.80 and 0.84, respectively for GMM-Gibbs-RP method and for GMM-EM-RP method the overall accuracy and kappa coefficient was 84.21 and 0.80, respectively. In other view point, in urban area (Pavia university dataset) with small structures, the amount of improvement in by Gibbs sampler in comparison with EM algorithm was more than the AVIRIS dataset which is related to agricultural area with bigger regions. This shows the capability of Gibbs sampler in confronting with singularities.

Due to the extent of mineral deposits, identification and proper management of resources is very important. According to the advent of remote sensing and specially producing hyperspectral remote sensing data which can get abundant spectral information, using this data for detailed study is rapidly expanding. Launch of the EO-1 in November 2000 introduced hyperspectral sensing of the earth from space through the Hyperion system. Hyperion has a single telescope and two spectrometers in visible near-infrared (VNIR) and short-wave infrared (SWIR). These spectral bands could provide abundant information about many important earth-surface minerals. Therefore one of the main aim of the present study was to examine the feasibility of the EO-1 Hyperion data in discriminating and mapping alteration zones around porphyry copper deposits (PCDs). The study area is situated at the Central Iranian Volcano-Sedimentary Complex, where the large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The visible near infrared and shortwave infrared (VNIR-SWIR) bands of data were used for image classifying and specially alteration mapping. The Pre-processing which was implemented on the level 1R Hyperion data in order to remove noise and acquire surface reflectance includes five steps that named removing uncalibrated bands, spatial displacement correction, destriping, spectral curvature (smile) correction and at last atmospheric correction. It is noticeable that atmospheric correction, because of using the target detection algorithm, SAM, is one of the most important step in this study. Therefore the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm, available in ENVI software, was implemented to obtain surface reflectance data. This algorithm is a MODTRAN4-based atmospheric correction software package designed to eliminate atmospheric effects through derivation of atmospheric properties such as surface albedo, surface altitude, water vapor column, aerosol, and cloud optical depths, as well as surface and atmospheric temperatures from hyperspectral data. In this paper Spectral Angle Mapper (SAM) and Gaussian Mixture Model (GMM) were implemented on pre-processed and calibrated Hyperion dataset. For using SAM algorithm, introducing reference spectra is obligatory. Information extraction from a Hyperion data set involves several processes including extraction of scene spectral endmembers using an integration of MNF, pixel purity index (PPI), and n-dimensional visualizer approaches. Then the extracted spectra which characterized using spectral analysis procedure available at ENVI and visual inspection, were used as reference for subsequent processing by SAM algorithm. On the other hand Gaussian mixture model (GMM) has been successfully used for HSI classification. It has also proved beneficial for a variety of classification tasks, such as speech and speaker recognition, clustering, etc. For estimating the parameters of GMM, the Expectation-Maximization (EM) algorithm was used. In order to compare and assess the accuracy of methods proposed in this study, a simulated data used to demonstrate the efficiency of algorithms which used in this study. Results revealed that Hyperion data prove to be powerful in discriminating and mapping various types of alteration zones while the data were subjected to adequate pre-processing. Overall accuracy and kappa coefficient for results of SAM and GMM are 82%, 0.75 and 80%, 0.71 respectively.

Nowadays, Earth observation (EO) technology became an indispensable tool to help environmental monitoring, as well as their changes, for natural resources management, urban planning and development, water management and land use planning. In particular, radar EOs, unlike the optical ones, can be collected regardless of illumination and weather conditions. Multitemporal polarimetric synthetic aperture radar (PolSAR) images are useful source of information for detection and mapping the environmental changes, especially in wide areas, during the day and night and all weather conditions. Change detection methods can identify the change or no change conditions in land covers using the time series observations. In this paper a method is proposed for change detection in SAR remote sensing images. This method is based on the Change Point Analysis. The cumulative frequency of difference image, which contains the environmental changes, normally follows a specific class of statistical distribution. Gaussian mixture model is one of the most suitable models for Change Point Analysis. This model can efficiently estimate the parameters of mixture distribution. The intersection point of two distributions is a change point, which can be seen as a threshold. This threshold is then used to separate the change and no change classes. The proposed method is implemented and analyzed using three SAR data sets. The analytical evaluations of the final change maps from two of these data sets with reference data had the Kappa coefficients of 90% and 96% respectively. The other data set contained the multitemporal PolSAR images and had been acquired over an agricultural area. The changes in these images were enough reliable to be connected to the agricultural activities, such as crop growing stages and harvesting, based on an available crop map. Finally, the method was evaluated against the Otsu method, as one of the best threshold estimation methods, and the results showed the superiority of the proposed method, e.g. 2% better in term of kappa coefficient. . As a result, the proposed method, can be efficiently employed for land cover change detection and monition in natural resources management.