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remote sensing image retrieval Based on 3D-Local Ternary Pattern(LTP)Features and Non-subsampled Shearlet Transform(NSST)Domain Statistical Features

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 137-164页

作者： Hilly Gohain Baruah Vijay Kumar Nath Deepika Hazarika Department of Electronics and Communication Engineering Tezpur UniversityAssam784028India

With the increasing popularity of high-resolution remote sensing images,the remote sensing image retrieval(RSIR)has always been a topic of major issue.A combined,global non-subsampled shearlet transform(NSST)-domain statistical features(NSSTds)and local three dimensional local ternary pattern(3D-LTP)features,is proposed for high-resolution remote sensing *** model the NSST image coefficients of detail subbands using 2-state laplacian mixture(LM)distribution and its three parameters are estimated using Expectation-Maximization(EM)*** also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband,and concatenate all of them with the 2-state LM parameters to describe the global features of the *** various properties of NSST such as multiscale,localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an *** order to extract the dense local features,a new 3D-LTP is proposed where dimension reduction is performed via selection of‘uniform’*** 3D-LTP is calculated from spatial RGB planes of the input *** proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured ***,a fused feature representation(NSSTds-3DLTP)is proposed using new global(NSSTds)and local(3D-LTP)features to enhance the discriminativeness of *** retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19,Aerial image Dataset(AID)and PatternNet in terms of mean average precision(MAP),average normalized modified retrieval rank(ANMRR)and precision-recall(P-R)*** experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB,Granulometry,loc

关键词： remote sensing image retrieval laplacian mixture model local ternary pattern statistical modeling,KS test,texture,global features

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An improved Bag-of-Words framework for remote sensing image retrieval in large-scale image databases

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International Journal of Digital Earth 2015年第4期8卷 273-292页

作者： Jin Yang Jianbo Liu Qin Dai Institute of remote sensing and Digital Earth Chinese Academy of Sciences BeijingChina University of Chinese Academy of Sciences BeijingChina

Due to advances in satellite and sensor technology,the number and size of remote sensing(RS)images continue to grow at a rapid *** continuous stream of sensor data from satellites poses major challenges for the retrieval of relevant information from those satellite *** Bag-of-Words(BoW)framework is a leading image search approach and has been successfully applied in a broad range of computer vision problems and hence has received much attention from the RS ***,the recognition performance of a typical BoW framework becomes very poor when the framework is applied to application scenarios where the appearance and texture of images are very *** this paper,we propose a simple method to improve recognition performance of a typical BoW framework by representing images with local features extracted from base *** addition,we propose a similarity measure for RS images by counting the number of same words assigned to *** compare the performance of these methods with a typical BoW *** experiments show that the proposed method has better recognition performance than that of the BoW and requires less storage space for saving local invariant features.

关键词： remote sensing image retrieval base image Bag-of-Words visual word

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