Recently, a promising pattern-recognition system has been presented to deal with the extraction of buried-object characteristics in ground-penetrating-radar images. In particular, it allows the detecting of buried obj...
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Recently, a promising pattern-recognition system has been presented to deal with the extraction of buried-object characteristics in ground-penetrating-radar images. In particular, it allows the detecting of buried objects by means of a search method based on genetic algorithms and the recognizing of the material type of the identified objects through a classification approach based on support vector machines. In this letter, we propose to extend the processing capabilities of this system by addressing the issue of the detected buried-object size estimation. This problem is viewed as a regression issue where it is aimed at reproducing the relationship between a set of opportunely extracted features and the object size. For such purpose, it is formulated within a Gaussian process (GP) regression approach. A detailed experimental study is reported, showing encouraging object-size-estimation accuracies even when buried objects are close to each other.
A new and general framework-called modified polynomial regression (MPR)-is introduced in this letter, which detects the changes that occurred in remotesensingimages. It is an improvement of the conventional polynomi...
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A new and general framework-called modified polynomial regression (MPR)-is introduced in this letter, which detects the changes that occurred in remotesensingimages. It is an improvement of the conventional polynomial regression (CPR) method. Most change detection (CD) methods, including CPR, do not consider the spatial relations among image pixels. To improve CPR, our proposed framework incorporates the spatial information into the CD process by using linear spatial-oriented image operators. It is proved that MPR preserves the affine invariance property of CPR. A realization of MPR is proposed, which employs the image derivatives to account for spatiality. Experimental results show the superiority of the proposed method over the CPR method and three other difference-based CD methods, namely, simple differencing, linear chronochrome CD, and multivariate alteration detection.
In this paper, the knowledge modeling, architecture design and detailed implementation of an ontology-based knowledge base for target recognition in remotesensingimages is presented. Knowledge base is a critical com...
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We derive a class of algorithms for detecting anomalous changes in hyperspectral image pairs by modeling the data with elliptically contoured (EC) distributions. These algorithms are generalizations of well-known dete...
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We derive a class of algorithms for detecting anomalous changes in hyperspectral image pairs by modeling the data with elliptically contoured (EC) distributions. These algorithms are generalizations of well-known detectors that are obtained when the EC function is Gaussian. The performance of these EC-based anomalous change detectors is assessed on real data using both real and simulated changes. In these experiments, the EC-based detectors substantially outperform their Gaussian counterparts.
Next to image sensors, future's robots will definitely use a variety of sensing mechanisms for navigation and prevention of risks to human life, for example flow-sensor arrays for 3D hydrodynamic reconstruction of...
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Next to image sensors, future's robots will definitely use a variety of sensing mechanisms for navigation and prevention of risks to human life, for example flow-sensor arrays for 3D hydrodynamic reconstruction of the near environment. This paper aims to quantify the possibilities of our artificial hair flow-sensor for high-resolution flow field visualization. Using silicon-on-insulator (SOI) technology with deep trench isolation structures, hair-based flow sensors with separate electrodes arranged in wafer-scale arrays have been successfully fabricated. Frequency Division Multiplexing (FDM) is used to interrogate individual hair elements providing simultaneous real-time flow measurements from multiple hairs. This is demonstrated by reconstructing the dipole fields along different array elements and hence localizing a dipole source relative to the hair array elements.
A new approach to the problems of statistical and structural patternrecognition, a signal processing and image analysis techniques has been considered. These problems are extremely important for tasks being solved by...
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ISBN:
(纸本)9780819483478
A new approach to the problems of statistical and structural patternrecognition, a signal processing and image analysis techniques has been considered. These problems are extremely important for tasks being solved by airborne and space borne remotesensing systems. Development of new remote sensors for image and signal processing is inherently connected with a possibility of statistical processing of images. Fundamentally new optoelectronic sensors "Multiscan" have been suggested in the present paper. Such sensors make it possible to form directly certain statistical estimates, which describe completely enough the different types of images. The sensors under discussion perform the Lebesgue-Stieltjes signal integration rather than the Cauchy-Riemann one. That permits to create integral functionals for determining statistical features of images. The use of the integral functionals for imageprocessing provides a good agreement of obtained statistical estimates with required image information features. The Multiscan remote sensors allows to create a set of integral moments of an input image right up to high-order integral moments, to form a quantile representation of an input image, which provides a count number limited texture, to form a median, which provides a localisation of a low-contrast horizon line in fog, localisation of water flow boundary etc. This work presents both the description of the design concept of the new remote sensor and mathematical apparatus providing the possibility to create input image statistical features and integral functionals.
The need to include areas around the target location into remotesensing predictions is being increasingly stressed. This paper introduces the LSTATS software for calculating local statistics both in the local kernel ...
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ISBN:
(纸本)9780889868236
The need to include areas around the target location into remotesensing predictions is being increasingly stressed. This paper introduces the LSTATS software for calculating local statistics both in the local kernel and as well as the segmented portions of an image. Ten special features not commonly used by the remotesensing community were found and described, and their potential application in forest remotesensing was presented. The "Weighted Moran's I", "Homogeneity of neighbours" and "Difference between centre and boundary" are examples of distinctive local statistics. Local statistics considered in this paper can be most helpful in forest remotesensing systems for distinguishing shadowed management passages, spruce canopies, groups of tree crowns and clearings in forests.
This work presents a new approach for automatic recognition of coffee crops in RSIs. The method applies an approach based on Genetic Programming (GP) to combine texture and spectral information encoded by image descri...
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ISBN:
(纸本)9781424495665
This work presents a new approach for automatic recognition of coffee crops in RSIs. The method applies an approach based on Genetic Programming (GP) to combine texture and spectral information encoded by image descriptors. Experiments show that the proposed method yields slightly better results than the traditional MaxVer approach.
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