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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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.
Vegetation indices (VIs) are essential parameters widely used in the biosphere remotesensing retrieval, and the relationship between the same vegetation indexes derived from different sensors is critical to long-term...
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ISBN:
(纸本)9780819483294
Vegetation indices (VIs) are essential parameters widely used in the biosphere remotesensing retrieval, and the relationship between the same vegetation indexes derived from different sensors is critical to long-term monitoring of land surface properties. In this paper, MSAVI data derived from visible and near-infrared data acquired by the ASTER and SPOT4 sensors were compared over the same time periods and pixel size. The results showed that the two VIs play a higher correlation in high data field. ASTER MSAVI is more sensitive to vegetation coverage information. SPOT MSAVI overvalues the local vegetation reflection signals significantly. The linear relationship between vegetation coverage and MSAVI requires field sampling data to complete correction.
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.
patternrecognition is an important step in map generalization. patternrecognition in street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which ...
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ISBN:
(纸本)9783642259258
patternrecognition is an important step in map generalization. patternrecognition in street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines with roughly right angle. Inspired by object recognition in imageprocessing, this paper presents an approach to the grid recognition in street network based on graph theory. Firstly, the bridges and isolated points of the network are identified and deleted repeatedly. Secondly, the similar orientation graph is created, in which the vertices represent street segments and the edges represent the similar orientation relation between streets. Thirdly, the candidates are extracted through graph operators such as finding connected component, finding maximal complete sub-graph, join and intersection. Finally, the candidate are evaluated by deleting bridges and isolated lines repeatedly, reorganizing them into stroke models, changing these stroke models into street intersection graphs in which vertices represent strokes and edges represent strokes intersecting each other, and then calculating the clustering coefficient of these graphs. Experimental result shows the proposed approach is valid in detecting the grid pattern in lower degradation situation.
Multi-temporal remotesensingimage registration is the key step of change detection, and because of the remarkable difference and the probably unknown of sensor parameters, the automatic registration of different tem...
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ISBN:
(纸本)9780819480798
Multi-temporal remotesensingimage registration is the key step of change detection, and because of the remarkable difference and the probably unknown of sensor parameters, the automatic registration of different temporal remotesensingimages is very difficult. image registration based on Fourier-Mellin transform( FMT) is a global and phase correlation method, which is based on Fourier and Log-polar transform. This method finds the transformation parameters for registration of the images while working in the frequency-domain and it is resilient to noise, occlusions and so on. In this paper, an improved approach based on Fourier-Mellin algorithm is proposed for the registration. Spectrum aliasing and resampling interpolation will bring errors during Fourier-Mellin transform. To get a better registration result, we have improved it by adding window function and filtering to reduce spectrum aliasing and increase the robustness.
In recent years, Multi-sources data fusion techniques have already been an International research hotspot in remotesensing. To date, many image fusion techniques have been developed. However, the available algorithms...
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ISBN:
(纸本)9780819483294
In recent years, Multi-sources data fusion techniques have already been an International research hotspot in remotesensing. To date, many image fusion techniques have been developed. However, the available algorithms can hardly produce a satisfactory fusion result for high resolution images. Among the existing fusion algorithms, the IHS technique is the most widely used one, and the wavelet fusion is the most frequently discussed one in recent publications because of its advantages over other fusion techniques. But the shortcome of colour distortion and low resolution in many field is often obvious, especially when QuickBird natural colour multispectral images are fused with its panchromatic images. In this paper, an improved fusion of IHS based on wavelet is proposed. At the same time, From the result of this experiment proves that the concept of the proposed improved fusion is promising, and it does significantly improve the fusion quality compared to conventional IHS and wavelet transform fusion techniques.
There are many uncertainties in image segmentation, which needs theories and methods with uncertainty to handle. This paper proposes a novel method of image segmentation based on data field and cloud model, which cons...
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ISBN:
(纸本)9780819483294
There are many uncertainties in image segmentation, which needs theories and methods with uncertainty to handle. This paper proposes a novel method of image segmentation based on data field and cloud model, which considers the spatial information of image through data field, and handles the uncertainty of image through cloud model. The proposed method inspired from cognitive physics considers each pixel as a physical object, calculates the interactive force of these physical objects, and generates image data field and the potential values which are considered as spatial information. And then, uses cloud transformation and magnitude cloud synthesis to extract the concepts of potential-frequency histogram from low level to high level, realizes the clustering of pixels, finally uses maximum determination to partition the pixels into different classes and segment image into different regions. Results of many experiments indicate that the proposed method obtains better effect than those of Fuzzy C-means clustering, Otsu and cloud based hierarchical method, and it is feasible and effective.
The limitations on spatial resolution and on the availability and measurement accuracy of remotesensingimages are the primary problems in the estimation of the large-scale planting area for *** integration of mid-an...
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ISBN:
(纸本)9783642272776
The limitations on spatial resolution and on the availability and measurement accuracy of remotesensingimages are the primary problems in the estimation of the large-scale planting area for *** integration of mid-and low-resolution images is the one of primary methods used for the estimation of large-scale crop planting areas using remote *** use of a single-temporal thematic mapper(TM)image results in a low accuracy of maize recognition,so a mid-scale time-series normalized difference vegetation index(NDVI)dataset,which was derived from the fusion of the moderate-resolution imaging spectroradiometer(MODIS)and TM images based on the wavelet transform,was *** planting area was estimated using the minimum distance model and the accuracy was evaluated using in-situ *** results show that the estimation of the maize-sown area based on the time-series NDVI information of the integrated images reached high levels of gross and position accuracy(89%and 90%),indicating that this method could fully utilize the time-series information from the MODIS images and the spatial resolution of a TM *** use of the difference in phenophases among fall crops enables the effective classification of the spatial distribution of these crops.
Adaptive coded aperture (diffraction) sensing, an emerging technology enabling real-time, wide-area IR/visible sensing and imaging, could benefit from new high performance biologically inspired imageprocessing archit...
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ISBN:
(纸本)9780819483140
Adaptive coded aperture (diffraction) sensing, an emerging technology enabling real-time, wide-area IR/visible sensing and imaging, could benefit from new high performance biologically inspired imageprocessing architectures. The memristor, a novel two terminal passive device can enable significantly powerful biologically inspired processing architectures. This device was first theorized by Dr. Leon Chua in 1971. In 2008, HP Labs successfully fabricated the first memristor devices. Due to its unique properties, the memristor can be used to implement neuromorphic functions as its dynamics closely model those of a synapse, and can thus be utilized in biologically inspired processing architectures. This paper uses existing device models to determine how device parameters can be tuned for the memristor to be used in neuromorphic circuit design. Specifically, the relation between the different models and the number of states the device can hold are examined.
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