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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Hypercube States for Sub-Planck Sensing
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： Crop classification,river basin,multisource remotely sensed data,phenology,time-series analysis,multiple classifiers,multiple scales,HJ-1/CCD,land cover
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Experimental Implementation of Non-uniformity Effects in Artificial Media : (Invited)
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： land cover,river basin,time-series analysis,multisource remotely sensed data,phenology,Crop classification,HJ-1/CCD,multiple scales,multiple classifiers
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - Singapore, Singapore (2019.11.25-2019.11.28)] 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - A Comparative Study of Flexible Power Point Tracking Algorithms in Photovoltaic Systems
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： HJ-1/CCD,multiple classifiers,phenology,river basin,multiple scales,time-series analysis,Crop classification,land cover,multisource remotely sensed data
更新于2025-09-16 10:30:52
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Polsar Image Crop Classification Based on Deep Residual Learning Network
摘要： PolSAR image classification is one of the most basic applications of polarimetric synthetic aperture radar (PolSAR) data, which is of great significance to the research and subsequent application of PolSAR data. Traditional PolSAR image classification methods, mainly based on a single type of target decomposition method, the dimension of feature used in the process of PolSAR image classification process is relatively less and cannot make full use of the abundant feature of the PolSAR image, which is the one of the most essential characteristics of PolSAR data. With the development of deep learning, an amount of excellent deep learning models is proposed, such as deep brief net, AlexNet, deep residual network (ResNet) and so on. The classification method based on deep learning is proved to be better than traditional methods in the classification of optical and SAR images. This paper mainly analyzes the application of ResNet model in PolSAR image classification, the effectiveness of the method was proved by comparing the classical PolSAR image classification method. Firstly, some target decomposition methods were selected to calculate the multi-dimensional feature image. Secondly, the sample points of different land cover types were manually selected, and the multi-dimensional features were extracted to form the experimental data samples. Then, the PolSAR classification model based on ResNet was constructed, and the model parameters were adjusted dynamically according to the experimental sample data. Finally, the trained model was applied to the classification of experimental data, and the accuracy of the model was assessed by calculating the Kappa index of the classification result. In this paper, a quantitative index is proposed to calculate the ability of each feature to distinguish different land cover types, and the weak distinguishing feature was deleted to reduce the influence of classification independent features on the model and to improved classification accuracy. As for the speckle noise, the PolSAR image was preprocessed by simple linear iterative clustering the experimental image was divided into a determined number of superpixel blocks, and the PolSAR image classification based on super-pixel blocks. Experimental results show that the PolSAR image classification method based on ResNet is conducive the comprehensive utilization of multi- dimensional features of PolSAR image, the classification accuracy of PolSAR image is better than that of the classic classification method. The optimization of feature sets is beneficial to reduce model training time and improve the classification accuracy of PolSAR image as well. The superpixel segmentation is beneficial to reduce speckle noise and further improves the accuracy of classification.
关键词： Simple linear iterative cluster,PolSAR image,Crop classification,Deep residual network,Feature optimization
更新于2025-09-11 14:15:04