Terrestrial and airborne LiDAR data are increasingly being used within a wide range of applications, from terrain mapping, city modeling to thematic applications such as forestry, hydrology, or geophysics. This specia...
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Terrestrial and airborne LiDAR data are increasingly being used within a wide range of applications, from terrain mapping, city modeling to thematic applications such as forestry, hydrology, or geophysics. This special issue of the ISPRS Journal of Photogrammetry and remotesensing addresses recent Advances in LiDAR data processing and applications. We were happy to have received many high-quality research papers falling within the following research areas: patternrecognition in urban areas Even though first operational uses have started to emerge, the automatic mapping of urban areas is still an area of active research.
The image classification process is based on the assumption that pixels which have similar spatial distribution patterns, or statistical characteristics, belong to the same spectral class. In a previous study we have ...
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ISBN:
(数字)9783642215933
ISBN:
(纸本)9783642215926;9783642215933
The image classification process is based on the assumption that pixels which have similar spatial distribution patterns, or statistical characteristics, belong to the same spectral class. In a previous study we have shown how we can improve the accuracy of classification of remotely sensed imagery data by incorporating contextual elevation knowledge in a form of a digital elevation model with the output of the classification process using Dempster-Shafer Theory of Evidence. A knowledge based approach is created for this purpose using suitable production rules derived from the elevation distributions and range of values for the elevation data attached to a particular satellite image. Production rules are the major part of knowledge representation and have the basic form: IF condition THEN Inference. Although the basic form of production rules has shown accuracy improvement, in general, in some cases accuracy can degrade. In this paper we propose a "refined" approach that takes into account the actual "distribution" of elevation values for each class rather than simply the "range" of values to solve the accuracy degradation. This approach is performed by refining the basic production rules used in the previous study taking into account the number of pixels at each elevation within the elevation distribution for each class.
Target recognition in remotesensingimage is a complicated patternrecognition task. In this paper we propose a new method of recognition of bridge over water in remotesensingimage. Various techniques were applied ...
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Target recognition in remotesensingimage is a complicated patternrecognition task. In this paper we propose a new method of recognition of bridge over water in remotesensingimage. Various techniques were applied to the process. image segmentation and mathematics morphology techniques were used to find out water areas and locate the ROIs(Regions of Interest), and then feature extraction and HNN were utilized to recognize the bridge. During the process, we also introduced a workable method of binary coding specific to Discrete Hopfield Neural Network. The results show that the process we propose is an effective method to fulfil the task. The main contribution of this paper lies in successfully applying Discrete Hopfield Neural Network, which few scholars used before, in recognition of bridge in remotesensingimage.
remotesensingimage segmentation is one of key factors which determine the success of remotesensingimage analysis and calculation. Information extraction and target recognition only can expand further and get bette...
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remotesensingimage segmentation is one of key factors which determine the success of remotesensingimage analysis and calculation. Information extraction and target recognition only can expand further and get better results based on obtained better segmentation results. But most algorithms at present for remotesensingimage segmentation are specific and there is no universal segmentation theory and algorithms. This paper aims to design the universal remotesensingimage segmentation parallel model preliminarily, using remotesensingimage parallel block strategy and imageprocessing chain in the circumstance of high performance cluster processing system.
Nowadays the measurement of the contact area of the foot has become an objective way of classifying feet and to describe the form of the longitudinal arch of the foot. In this work, the implementation of a digital pod...
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ISBN:
(纸本)9780819489340
Nowadays the measurement of the contact area of the foot has become an objective way of classifying feet and to describe the form of the longitudinal arch of the foot. In this work, the implementation of a digital podoscope that enables remotesensing for evaluation of the foot is described. The podogram includes an outline of the plantar pressure, and the contour of the arch. This method of evaluation is relatively simple and inexpensive while maintaining precision. The recognition system compares the image of the actual foot with that of a reference image. Information from the comparison can be sent by internet to obtain a remote diagnosis from an expert.
In the process of dealing with traffic accidents, the contour of the car body parts is very important to the information of the vehicle body. By using imageprocessing techniques and methods, this paper proposed a new...
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In the process of dealing with traffic accidents, the contour of the car body parts is very important to the information of the vehicle body. By using imageprocessing techniques and methods, this paper proposed a new type recognition system of rear-view mirror outline. We also designed the way of active and passive vision measurement in the image acquisition phase; discussed the way of using the Harsdorff distance of the fragments and the standard parts' profile curve for image matching; and designed a database to achieve the query and comparison of fragments and standard parts.
A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distri...
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A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distributed false discovery rate hypotheses test in statistics to set the threshold in the NSCT domain, and then removes the noise through soft threshold function, which doesn’t depend on the length of signal. The experimental results show that the proposed method can more effectively reduce Gaussian noise and improve the peak value signal-to-noise ratio in the remotesensingimage; Meanwhile, this method utilizes the shift invariant of NSCT transform to inhibit the pseudo Gibbs distortion effect, and integrally preserves the texture and edge etc.. details’ information of the image, thus obviously ameliorate the visual effect of the image.
Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buri...
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ISBN:
(纸本)9780819485915
Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of patternrecognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that non-linear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.
This work proposes a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method uses HMM to relate the varying spectral response along the crop cycle with plant phenology, for different crop ...
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Classification system and textural features play increasingly an important role in remotely sensed images classification and many patternrecognition applications. In this work, we propose to fuse the information outp...
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