In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in thi...In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.展开更多
A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural fea^res were extracted by volumetric gray-level...A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural fea^res were extracted by volumetric gray-level co-occurrence matrices （VGLCM）. The spectral features were extracted by minimum estimated abundance covar- iance （MEAC） and linear prediction （LP）-based band selection, and a semi-supervised k-means （SKM） cluster- ing method with deleting the worst cluster （SKMd） band- clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classifica- tion by using spectral and textural features. It has been proven that the proposed method using VGLCM outper- forms the gray-level co-occurrence matrices （GLCM） method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.展开更多
基金sponsored by National Key R&D Program of China(2018YFC1504504)Youth Foundation of Yunnan Earthquake Agency(2021K01)Project of Yunnan Earthquake Agency“Chuan bang dai”(CQ3-2021001).
文摘In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.
文摘A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural fea^res were extracted by volumetric gray-level co-occurrence matrices （VGLCM）. The spectral features were extracted by minimum estimated abundance covar- iance （MEAC） and linear prediction （LP）-based band selection, and a semi-supervised k-means （SKM） cluster- ing method with deleting the worst cluster （SKMd） band- clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classifica- tion by using spectral and textural features. It has been proven that the proposed method using VGLCM outper- forms the gray-level co-occurrence matrices （GLCM） method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.
