Significant point (SP) detection is an important pre-processing step in image registration, data fusion, object recognition and in many other tasks. This paper deals with multiframe SP detection, i.e. detection in two...
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Significant point (SP) detection is an important pre-processing step in image registration, data fusion, object recognition and in many other tasks. This paper deals with multiframe SP detection, i.e. detection in two or more images of the same scene which are supposed to be blurred, noisy, rotated and shifted with respect to each other. We present a new method invariant under rotation that can handle differently blurred images. Thanks to this, the point sets extracted from different frames have relatively high number of common elements. This property is highly desirable for further multiframe processing. The performance of the method is demonstrated experimentally on satellite images. (C) 1999 Elsevier Science B.V. All rights reserved.
image segmentation is very important for patternrecognition, ROI compression and information collection in remotesensing studies. In this paper, we propose an efficient image segmentation algorithm by using the Disc...
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image segmentation is very important for patternrecognition, ROI compression and information collection in remotesensing studies. In this paper, we propose an efficient image segmentation algorithm by using the Discrete Wavelet Frame Transform (DWFT) and Multiresolution Markov Random Field (MMRF). This algorithm avoids the over-segmentation that is common in other segmentation algorithms. Our experiments show that the proposed algorithm is very robust and it can be successfully used under noisy conditions.
Recently oblique projection has been studied for many applications in signal processing. In this paper, the concept of oblique projection is applied to develop an algorithm for hyperspectral image classification. Comp...
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Recently oblique projection has been studied for many applications in signal processing. In this paper, the concept of oblique projection is applied to develop an algorithm for hyperspectral image classification. Compared with the orthogonal subspace projector (OSP), it can be found that OSP is a priori classifier but the oblique subspace projection classifier will be referred to a posterior. As a consequence, the oblique subspace projector (OBP) can be thought of as a generalized classifier including OSP. Furthermore, the estimation error from the OBP can be evaluated by applying the Neyman-Pearson detection theory to the corresponding receiver operating characteristic (ROC) curve so the accuracy of the classification can be calculated thereafter. Finally, some computer simulations using real airborne visible infrared image spectrometer (AVIRIS) data are accomplished to justify and compare the effectiveness of the above algorithms. (C) 1999 patternrecognition Society. Published by Elsevier Science Ltd. All rights reserved.
This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechn...
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This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechniques are described systematically about winter wheat yield estimation system, including automatically extractingwheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. patternrecognition technique was applied to extract sown area using TM data. Temporal NDVI(Normal Division Vegetation Index) profiles were produced from 8 - 12 times AVHRR data during wheat growth dynamically. A remotesensing yield model for large area was developed based on greenness accumulation, temperature andgreenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which consists of 4 subsystems, namely databases management, imageprocessing, models bank management and production prediction *** accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.
作者:
Garcia-Consuegra, J.Cisneros, G.Martinez, A.
Castilla-La Mancha University Campus Universitario s/n 02071 Albacete Spain Grupo de Tratamiento de Imágenes
Universidad Politécnica de Madrid Ciudad Universitaria 28040 Madrid Spain
Castilla-La Mancha University Campus Universitario s/n 02071 Albacete Spain
In this paper we provide a solution to a common problem in remotesensing when woody crops (almond and olive fields, vineyards, and so on) must be located and discriminated. Experience has taught us that, currently, t...
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Thermal hyperspectral imagery introduces new possibilities in remotesensing. This paper deals with testing the accuracy of the supervised classification of the artificial objects in the thermal hyperspectral imagery,...
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As a data structure, a tree is an optimal presentation of hierarchical objects. Many irregular and dynamical phenomena studied for example in biology, medical sciences, meteorology, and geomorphology can be modelled a...
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Hyperspectral image sets are three dimensional data volumes that are difficult to exploit by manual means because they are comprised of multiple bands of image data that are not easily visualized or assessed. GTE Gove...
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Hyperspectral image sets are three dimensional data volumes that are difficult to exploit by manual means because they are comprised of multiple bands of image data that are not easily visualized or assessed. GTE Government Systems Corporation has developed a system that utilizes Evolutionary Computing techniques to automatically identify materials in terrain hyperspectral imagery. The system employs sophisticated signature preprocessing and a unique combination of non-parametric search algorithms guided by a model based cost function to achieve rapid convergence and patternrecognition. The system is scaleable and is capable of discriminating and identifying pertinent materials that comprise a specific object of interest in the terrain and estimating the percentage of materials present within a pixel of interest (spectral unmixing). The method has been applied and evaluated against real hyperspectral imagery data from the AVIRIS sensor. In addition, the process has been applied to remotely sensed infrared spectra collected at the microscopic level to assess the amounts of DNA, RNA and protein present in human tissue samples as an aid to the early detection of cancer.
The AMOVIP project is a joint initiative for a cooperation in specific aspects of vision research and their applications, involving EU partners in Spain and Portugal together with developing countries Mexico and Brazi...
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Mathematical morphology coupled with creation of a time stack image and principal oscillation pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. ...
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