The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Base...The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.展开更多
Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, an...Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.展开更多
The development of high-resolution remote sensing imaging technology provides a new way to the large-scale estimation of forest canopy density. The traditional inversion methods for canopy density only use spectral or...The development of high-resolution remote sensing imaging technology provides a new way to the large-scale estimation of forest canopy density. The traditional inversion methods for canopy density only use spectral or topographical features of remote sensing images.However,due to the existence of the different thing with same spectrum and the same thing with different spectrum phenomena,it is difficult to improve the estimation accuracy of canopy density.Based on spectrum and other traditional features,this paper combines texture features of remote sensing images to estimate canopy density.Firstly,the gray level co-occurrence matrix (GLCM) texture features are computed using objectbased method.Then,the principal component analysis (PCA) method is applied in correlation analysis and dimension reduction of texture features.Finally, spectrum and topographical features together with texture features are introduced into stepwise regression model to estimate canopy density.The experimental results showed that compared with the traditional method only based on spectrum or topographical features,the method combined with texture features greatly improved the estimation accuracy.The coefficient of determination(adjusted R^2 ) increased from 0.737 to 0.805.The estimation accuracy increased from 81.03%to 84.32%.展开更多
Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cov...Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calculation of R^2 values,the textural features of mean, homogeneity and correlation were chosen to be best combination for the size of 19×19 and the combination of skewness,homogeneity and mean was considered the most properly for the moving window size 19×19.Precision assessment of different textural combinations showed that VA,HO, CR combination with optimal moving window size (from 3×3 to 31×31) could evidently improve the classification precision for high-resolution remote sensing image.And the combination of mean,homogeneity,skewness,and contrast texture factors correlation can effectively reduce data redundancy,which obtained the similar results.In the texture features,the mean is the most important factor and impacts the classification of the tree species.This method could solve problems of forestry type classification,tree species classification,etc.It is much better than traditional method of based on pixel values.This procedure effectively reduced data redundancy and could assist in tree species classification.展开更多
文摘The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.
基金the auspices of the National Natural Science Foundation of China (Grant Nos. 41601408, 41601411)Shandong University of Science and Technology Research Fund (No. 2019TDJH103).
文摘Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.
文摘The development of high-resolution remote sensing imaging technology provides a new way to the large-scale estimation of forest canopy density. The traditional inversion methods for canopy density only use spectral or topographical features of remote sensing images.However,due to the existence of the different thing with same spectrum and the same thing with different spectrum phenomena,it is difficult to improve the estimation accuracy of canopy density.Based on spectrum and other traditional features,this paper combines texture features of remote sensing images to estimate canopy density.Firstly,the gray level co-occurrence matrix (GLCM) texture features are computed using objectbased method.Then,the principal component analysis (PCA) method is applied in correlation analysis and dimension reduction of texture features.Finally, spectrum and topographical features together with texture features are introduced into stepwise regression model to estimate canopy density.The experimental results showed that compared with the traditional method only based on spectrum or topographical features,the method combined with texture features greatly improved the estimation accuracy.The coefficient of determination(adjusted R^2 ) increased from 0.737 to 0.805.The estimation accuracy increased from 81.03%to 84.32%.
文摘Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calculation of R^2 values,the textural features of mean, homogeneity and correlation were chosen to be best combination for the size of 19×19 and the combination of skewness,homogeneity and mean was considered the most properly for the moving window size 19×19.Precision assessment of different textural combinations showed that VA,HO, CR combination with optimal moving window size (from 3×3 to 31×31) could evidently improve the classification precision for high-resolution remote sensing image.And the combination of mean,homogeneity,skewness,and contrast texture factors correlation can effectively reduce data redundancy,which obtained the similar results.In the texture features,the mean is the most important factor and impacts the classification of the tree species.This method could solve problems of forestry type classification,tree species classification,etc.It is much better than traditional method of based on pixel values.This procedure effectively reduced data redundancy and could assist in tree species classification.
