Compressive sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising re...
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Compressive sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising results for signal compression and reconstruction. However, their potential as dimensionality reduction techniques for time series has not been significantly explored to date. To this aim, this work investigates the suitability of compressive-sensed time series in an application of human action recognition. In the paper, results from several experiments are presented: (1) in a first set of experiments, the time series are transformed into the CS domain and fed into a hidden Markov model (HMM) for action recognition, (2) in a second set of experiments, the time series are explicitly reconstructed after CS compression and then used for recognition, (3) in the third set of experiments, the time series are compressed by a hybrid CS-Haar basis prior to input into HMM, (4) in the fourth set, the time series are reconstructed from the hybrid CS-Haar basis and used for recognition. We further compare these approaches with alternative techniques such as sub-sampling and filtering. Results from our experiments show unequivocally that the application of CS does not degrade the recognition accuracy, rather, it often increases it. This proves that CS can provide a desirable form of dimensionality reduction in patternrecognition over time series.
Linear pushbroom cameras are widely used in passive remotesensing from space as they provide high resolution images. In earth observation applications, where several pushbroom sensors are mounted in a single focal pl...
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ISBN:
(纸本)9781424469840;9781424469857
Linear pushbroom cameras are widely used in passive remotesensing from space as they provide high resolution images. In earth observation applications, where several pushbroom sensors are mounted in a single focal plane, small dynamic disturbances of the satellite's orientation lead to noticeable geometrical distortions in the images. In this paper, we present a global method to estimate those disturbances, which are effectively vibrations. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. To do so, we embed the estimation process in a Bayesian framework. An autoregressive model is used as a prior on the vibrations. The problem can be seen as a global image registration task where multiple pushbroom images are registered to the same coordinate system, the registration parameters being the vibration coefficients. An alternating maximisation procedure is designed to obtain Maximum a Posteriori estimates (MAP) of the vibrations as well as of the autoregressive model coefficients. We illustrate the performance of our algorithm on various datasets of satellite imagery.
The aim of this study is to extract homogenous and edge regions from a post-earthquake Quickbird satellite image with high resolution and to combine this spatial information with spectral information in classification...
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The aim of this study is to extract homogenous and edge regions from a post-earthquake Quickbird satellite image with high resolution and to combine this spatial information with spectral information in classification of earthquake damage. In order to extract the homogenous and edge regions from the image, a spatial filtering approach and Canny filter were used. A novel method called support vector selection and adaptation (SVSA) was used in classification of earthquake damage. Pixel and texture-based classification were separately carried out in order to show their comparative classification performance. For implementation, a small region from city of Bam in Iran was selected.
Many ore deposits are first detected in the field by the recognition of hydrothermally altered host rocks, and are typically zonally distributed. Ore deposits are often produced by fluid flow processes that alter the ...
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ISBN:
(纸本)9789604742035
Many ore deposits are first detected in the field by the recognition of hydrothermally altered host rocks, and are typically zonally distributed. Ore deposits are often produced by fluid flow processes that alter the mineralogy and chemistry of the country rock. One of the main reason for extention using a multi-spectral and hyperspectral sensor is due to detect the optical characteristics of the Earth's surface using several of spectral bands. All previous studies show that remotesensing has a important impress to detection alteration zones. The Advanced Spaceborne Thermal Emission and Reflection Radimeter (ASTER) sensor measures reflected radiation in VNIR, SWIR and TIR electromagnetic energies. It is cheap and easily available. The alteration minerals in Siyahrud area have been successfully investigated in the field and have been successfully detected by processing of Aster data. The finding shows hydrothermal alteration, which can be a model in indicating the productive units in this region. This Alteration mapping have been used by principal component analysis method, band ratio and False Color Composit method. this study and field investigation shows the hydrothermal alteration zone related to: iron oxide-bearing & hydroxidebearing minerals and mineral endmembers related to epithermal gold include phyllosilicates minerals ( Kaolinite,Illite, Alunite minerals). Results indicates ASTER,s capability to provide information on alteration minerals which are importance for mineral exploration activatites.
In this paper,a new approach of synthetic aperture radar(SAR) image target recognition based on non-negative matrix factorization(NMF) feature extraction and Bayesian decision fusion is presented for recognizing groun...
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In this paper,a new approach of synthetic aperture radar(SAR) image target recognition based on non-negative matrix factorization(NMF) feature extraction and Bayesian decision fusion is presented for recognizing ground vehicles in MSTAR ***,feature vectors are extracted from image chips by NMF *** vector machine(SVM) is used to classify the feature *** multiple views of the same vehicle collected at different aspects are classified by SVM,the outputs are fused by Bayesian decision fusion algorithm and then the final classification decision is *** evaluate NMF algorithm and the Bayesian decision fusion *** results indicate that there are significant target recognition performance benefits in the probability of correct classification when NMF algorithm is applied and three or more views are used for Bayesian decision fusion.
Besides several other factors, radiometric differences between a reference and a floating image greatly influence the achievable accuracy of image registration. In this work we derive the magnitude of registration ina...
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ISBN:
(纸本)9781424475421
Besides several other factors, radiometric differences between a reference and a floating image greatly influence the achievable accuracy of image registration. In this work we derive the magnitude of registration inaccuracy coming from changes in radiometric properties. This is done for the example of medical X-ray image registration. We therefore estimate the change of image intensity with respect to object shape, X-ray attenuation of the object material and the initial X-ray energy by modeling a simplified image formation process. The change in intensity is then used to determine a closed form estimation of the resulting registration error, independent from a specific registration algorithm. Finally the theoretical calculations are compared to the accuracy of intensity based registration performed on X-ray images with different radiometric properties. Results show that the herewith derived accuracy estimation is well suited to predict the achievable accuracy of a registration for images with radiometric differences.
Space-surface bistatic synthetic aperture radar (SS-BSAR) has gained more and more researcher's interests. In this paper, we focus on improving range resolutions of GNSS illuminator based SS-BSAR with the method o...
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Space-surface bistatic synthetic aperture radar (SS-BSAR) has gained more and more researcher's interests. In this paper, we focus on improving range resolutions of GNSS illuminator based SS-BSAR with the method of spectrum synthesis of ultra-wide-band (UWB). Narrow bandwidth signals, such as GPS P code, can be spliced into a wider signal. Simulation results show that it can effectively increase signal processing bandwidth, improve range resolution and target recognition capabilities.
Landslide is a type of mass movement that causes damage in many areas. The evolving remotesensing technology in producing high resolution images may help in landslide studies. However, the problem in detecting small ...
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Landslide is a type of mass movement that causes damage in many areas. The evolving remotesensing technology in producing high resolution images may help in landslide studies. However, the problem in detecting small size landslides is still challenging when suitable image resolution of the area being analyzed is not available. In this paper, a novel method based on elastic image registration, appropriate for the detection of small landslides will be presented. This method can be used to detect and quantify landslide movement with sub-pixel accuracy. It is based on the invocation of deformation operators which imitate the deformations expected to be observed when a landslide occurs. The similarity between two images is measured by a similarity function which takes into consideration grey level value correlation and geometric deformation. The geometric deformation term ensures that the minimum necessary deformation compatible with the two images is employed. An extra term, ensuring maximum overlap between the two images is also incorporated. There are two versions of this method. One using the correlation coefficient as a measure of similarity for the grey level value, and another one using mutual information. These methods are tested using known small scale landslides images of southern Italy taken from the Landsat 5 TM. The mutual information-based method gives more reliable results.
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