Soil erosion is a global environmental problem. The rapid monitoring of the coverage changes in and spatial patterns of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at regional scales can hel...
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Soil erosion is a global environmental problem. The rapid monitoring of the coverage changes in and spatial patterns of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at regional scales can help improve the accuracy of soil erosion evaluations. Three deep learning semantic segmentation models, DeepLabV3+, PSPNet, and U-Net, are often used to extract features from unmanned aerial vehicle (UAV) images;however, their extraction processes are highly dependent on the assignment of massive data labels, which greatly limits their applicability. At the same time, numerous shadows are present in UAV images. It is not clear whether the shaded features can be further classified, nor how much accuracy can be achieved. This study took the Mu Us Desert in northern China as an example with which to explore the feasibility and efficiency of shadow-sensitive PV/NPV classification using the three models. Using the object-oriented classification technique alongside manual correction, 728 labels were produced for deep learning PV/NVP semantic segmentation. ResNet 50 was selected as the backbone network with which to train the sample data. Three models were used in the study;the overall accuracy (OA), the kappa coefficient, and the orthogonal statistic were applied to evaluate their accuracy and efficiency. The results showed that, for six characteristics, the three models achieved OAs of 88.3-91.9% and kappa coefficients of 0.81-0.87. The DeepLabV3+ model was superior, and its accuracy for PV and bare soil (BS) under light conditions exceeded 95%;for the three categories of PV/NPV/BS, it achieved an OA of 94.3% and a kappa coefficient of 0.90, performing slightly better (by similar to 2.6% (OA) and similar to 0.05 (kappa coefficient)) than the other two models. The DeepLabV3+ model and corresponding labels were tested in other sites for the same types of features: it achieved OAs of 93.9-95.9% and kappa coefficients of 0.88-0.92. Compared with traditional machin
This study compared two object-oriented land use change detection methods, namely detection after classification (DAC) and classification after detection (CAD) using digital elevation model, slope data, multi-temporal...
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This study compared two object-oriented land use change detection methods, namely detection after classification (DAC) and classification after detection (CAD) using digital elevation model, slope data, multi-temporal Thematic Mapper (TM) images in 2000 and Enhanced TM Plus images (ETM+) in 2010. Results revealed that the accuracy of the former was higher than that of the latter. A minimal discrepancy was observed in the accuracies of the two methods in detecting deciduous broad-leaved, evergreen coniferous and mixed forests;uplands;paddies;bare lands and human habitation, which can be extracted using spectral indices due to large spectrum differences. The accuracy of DAC was higher than that of CAD for areas with similar spectra, such as industrial and traffic lands, green shrubs, rivers, channels, reservoirs/pools and lake, because the former can fully utilise various information, including spectral, spatial, structural, textural and contextual information, during the two-stage classification. Moreover, the change area boundary was not limited and adjustable during the classification process. DAC can overcome the smoothing effect to a great extent using multi-scale segmentations and multiple indicators for detection. Although DAC can yield better results than CAD can, the former was more time-consuming than the latter because of its two-stage classification. Therefore, the hybrid of the two methods could be the appropriate approach.
In recent years, the quick upgrading and improvement of SAR sensors provide beneficial complements for the traditional optical remote sensing in the aspects of theory, technology and data. In this paper, Sentinel-1 A ...
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In recent years, the quick upgrading and improvement of SAR sensors provide beneficial complements for the traditional optical remote sensing in the aspects of theory, technology and data. In this paper, Sentinel-1 A SAR data and GF-1 optical data were selected for image fusion, and more emphases were put on the dryland crop classification under a complex crop planting structure, regarding corn and cotton as the research objects. Considering the differences among various data fusion methods, the principal component analysis(PCA), Gram-Schmidt(GS), Brovey and wavelet transform(WT) methods were compared with each other, and the GS and Brovey methods were proved to be more applicable in the study area. Then, the classification was conducted based on the object-oriented technique process. And for the GS, Brovey fusion images and GF-1 optical image, the nearest neighbour algorithm was adopted to realize the supervised classification with the same training samples. Based on the sample plots in the study area, the accuracy assessment was conducted subsequently. The values of overall accuracy and kappa coefficient of fusion images were all higher than those of GF-1 optical image, and GS method performed better than Brovey method. In particular, the overall accuracy of GS fusion image was 79.8%, and the Kappa coefficient was 0.644. Thus, the results showed that GS and Brovey fusion images were superior to optical images for dryland crop classification. This study suggests that the fusion of SAR and optical images is reliable for dryland crop classification under a complex crop planting structure.
In recent years, remote sensing technology has been widespread applied in coastal resources survey and ecological environment research. The methods of coastal object extraction have been steadily innovating. Compared ...
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ISBN:
(纸本)9780769539423
In recent years, remote sensing technology has been widespread applied in coastal resources survey and ecological environment research. The methods of coastal object extraction have been steadily innovating. Compared with traditional manual interpretation and classification based on pixel, the object-oriented technique can increase classification accuracy and reduce the salt - pepper noise considerably. With the increase of the spatial reference of remote sensing and more textural feature, the object-oriented technique has broad application prospects. In this article, object-oriented method is applied to extract aquaculture water using CBERS-02B image. The method includes the following: image segmentation, segment merging, spectral and spatial features analyses, other water body elimination. The result shows that the method is effective in accurately extracting aquaculture water information by using CBERS-02B data.
object management Petri net technique is developed to integrate analysis, design and implementation for applications of management information system. Not only object concept is adopted to describe tokens in places, b...
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ISBN:
(纸本)9783038350194
object management Petri net technique is developed to integrate analysis, design and implementation for applications of management information system. Not only object concept is adopted to describe tokens in places, but also class inheritance and composition, user interface and complicate algorithm are represented by expanded transitions. Three kinds of working modes of object management Petri net are introduced to fulfill development, execution, and simulation of the net system.
The main objective of this study is to establish a rapid seismic risk analysis system for the existing bridges based on the pre-calculated fragility curves of more than 2000 bridges in Taiwan. In order to get the peak...
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The main objective of this study is to establish a rapid seismic risk analysis system for the existing bridges based on the pre-calculated fragility curves of more than 2000 bridges in Taiwan. In order to get the peak ground accelerations at sites of different bridges located in Taiwan resulted from each different earthquake, the attenuation law of peak ground accelerations for each station was also proposed based on the regression analysis on recorded earthquakes in Taiwan. The accuracy of the simulated results was also verified through the comparison with the data collected by Central Weather Bureau's in the past three years. In this study, the object-oriented programming is also incorporated to develop a seismic risk assessment system, which can instantly estimate and visually demonstrate the level of damage to bridges due to a specific seismic event and the corresponding economic loss due to the damage of bridges. The achievements attained from this study may be helpful for earthquake protection strategy in the future.
A general framework of hydraulic fault diagnosis system was studied. It consisted of equipment knowledge bases, databases, fusion reasoning, knowledge acquisition and so on. The tree-structure model of the fault knowl...
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ISBN:
(纸本)9783037855461
A general framework of hydraulic fault diagnosis system was studied. It consisted of equipment knowledge bases, databases, fusion reasoning, knowledge acquisition and so on. The tree-structure model of the fault knowledge was established based on fault hierarchy and logicality. Fault nodes knowledge was encapsulated by object-oriented technique. Complete knowledge bases were made including fault bases and diagnosis bases. It could describe the fault positions, system structure, cause-symptom relationships, diagnosis principles and other knowledge. The results show that the methods are effective.
In order to improve accuracy and convergence speed for flight trajectory optimization program in flight management computer and enhance its maintainability, an improved particle swarm optimization (PSO) algorithm with...
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In order to improve accuracy and convergence speed for flight trajectory optimization program in flight management computer and enhance its maintainability, an improved particle swarm optimization (PSO) algorithm with object-oriented performance database is proposed. Firstly, an object-oriented performance database is built by Microsoft Visual C++ and MATLAB/SIMULINK mixed software development environment. Through synthetically use class hierarchy and specialized function library, the flight performance data is retrieved and its data file can be replaced with adapt for different aircraft types. Secondly, the mass point motion mathematical model is built according to mass point dynamics and energy states. objective functions for trajectory optimization in vertical flight profile are acquired by the Minimum Principle of Pontryagin. Thirdly, adaptive inertia weight is introduced, the equality constraints is processed using the penalty function method. Finally, trajectory in vertical flight profile is optimized through using the improved PSO based on the object-oriented performance database. Meanwhile, the PSO algorithm flow for vertical flight profile trajectory optimization is given. Through using of the improved PSO, trajectory optimization of Boeing 737-800 aircraft in vertical flight profile is carried out. Comparison results between optimization results and flight test data show that the calculated results of proposed algorithm rapidly converges to optimal solution with higher precision.
Behavior is a fundamental aspect of object-oriented systems, but the modeling of their behavioral semantics has proved to be a great challenge. Currently existing techniques such as statecharts, interaction diagrams, ...
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Behavior is a fundamental aspect of object-oriented systems, but the modeling of their behavioral semantics has proved to be a great challenge. Currently existing techniques such as statecharts, interaction diagrams, activity diagrams, when applied to information systems and especially when precise specification of static and dynamic constraints is desired, appear to have some deficiencies. The paper presents obligation nets, a kind of high-level Petri net, as a technique to capture the required behavior of the system. The technique produces models that are both unambiguous and intuitive (thus easy to use). A formal semantics for obligation nets will be given in terms of colored Petri nets. Finally, examples will be given to illustrate practical uses of the proposed technique.
In recent years, remote sensing images with high resolution are increasingly applied in change detection and disaster assessment. Compared with the traditional pixel-based methods, object-oriented image processing tec...
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ISBN:
(纸本)9781457710056
In recent years, remote sensing images with high resolution are increasingly applied in change detection and disaster assessment. Compared with the traditional pixel-based methods, object-oriented image processing techniques have attracted more attention for high resolution images. In this paper, we aim to research the object-oriented change detection for urban area. A new multi-scale segmentation algorithm is proposed so as to obtain accurate image objects, and a pre-processing step is adopted to improve the computation efficiency. In order to testify the performance of proposed method, experiments are conducted on QuickBird images. The experimental results show that accurate image objects and changed area can be acquired in appropriate scales.
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