Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classif...Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.展开更多
Remote sensing technique, replacing conventional sonar bathymetry technique, has become an effective complementary method of mapping submarine terrain where special conditions make the sonar technique difficult to be ...Remote sensing technique, replacing conventional sonar bathymetry technique, has become an effective complementary method of mapping submarine terrain where special conditions make the sonar technique difficult to be carried out. At the same time, as one kind of data set, multispectral remote sensing data has the disadvantage of being influenced by the variable bottom types in shallow seawater, when it is applied in bathymetry. This paper puts forward a new method to extract water depth information from multispectral data, considering the bottom classification and the true water depth accuracy. That is the Principal Component Analysis (PCA) technique based on the bottom classification. By the least square regression with significance, the experiment near Qingdao City has obtained more satisfactory bathymetry accuracy than that of the traditional single-band method, with the mean absolute error about 2.57m.展开更多
Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in...Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.展开更多
The purpose of remote sensing images fusion is to produce a fused image that contains more clear,accurate and comprehensive information than any single image.A novel fusion method is proposed in this paper based on no...The purpose of remote sensing images fusion is to produce a fused image that contains more clear,accurate and comprehensive information than any single image.A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform(NSCT) and region segmentation.Firstly,the multispectral image is transformed to intensity-hue-saturation(IHS) system.Secondly,the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT.Then the NSCT coefficients of high and low frequency subbands are fused by different rules,respectively.For the high frequency subbands,the fusion rules are also unalike in the smooth and edge regions.The two regions are segregated in the panchromatic image,and the segmentation is based on particle swarm optimization.Finally,the fusion image can be obtained by performing inverse NSCT and inverse IHS transform.The experimental results are evaluated by both subjective and objective criteria.It is shown that the proposed method can obtain superior results to others.展开更多
The precision of Aster data is higher than that of Landsat series of multispectral remote sensing data,which can more accurately reveal the distribution of altered minerals.It plays an important role in prospecting,bu...The precision of Aster data is higher than that of Landsat series of multispectral remote sensing data,which can more accurately reveal the distribution of altered minerals.It plays an important role in prospecting,but it is rarely used in areas with complex terrain and high vegetation coverage.Based on this purpose,this study used Aster remote sensing data,and took Gongchangling iron deposit as a case study.It combined the mineral spectrum theory and the basic geologic data of the study area,using the model of principal component analysis(PCA)and color synthesis to extract abnormal altered minerals.The results show that the distribution of identified anomalies is basically consistent with the existing geological data in this study area,which provides a reliable reference for the mineral resources ex-ploration and delineation of mining areas.展开更多
The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust mo...The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To improve the accuracy of core tensor coding,the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in image.When applied to MS-RSI restoration,our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.展开更多
基金supported by the National Natural Science Foundation of China[grant number 41930104]by the Research Grants Council of Hong Kong[grant number PolyU 152219/18E].
文摘Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.
基金Foundation item: Under the auspices of Scientific Foundation Research Project of the Ministry of Science and Technology and Chinese Academy of Surveying and Mapping (No. F0610)
文摘Remote sensing technique, replacing conventional sonar bathymetry technique, has become an effective complementary method of mapping submarine terrain where special conditions make the sonar technique difficult to be carried out. At the same time, as one kind of data set, multispectral remote sensing data has the disadvantage of being influenced by the variable bottom types in shallow seawater, when it is applied in bathymetry. This paper puts forward a new method to extract water depth information from multispectral data, considering the bottom classification and the true water depth accuracy. That is the Principal Component Analysis (PCA) technique based on the bottom classification. By the least square regression with significance, the experiment near Qingdao City has obtained more satisfactory bathymetry accuracy than that of the traditional single-band method, with the mean absolute error about 2.57m.
基金supported by the National Natural Science Foundation of China(No. 32101280)the Natural Science Foundation of Shanghai(No. 21ZR1420900)the Key R&D Project of Zhejiang(No. 2023C03138)
文摘Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.
基金the National Natural Science Foundation of China (No.60872065)
文摘The purpose of remote sensing images fusion is to produce a fused image that contains more clear,accurate and comprehensive information than any single image.A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform(NSCT) and region segmentation.Firstly,the multispectral image is transformed to intensity-hue-saturation(IHS) system.Secondly,the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT.Then the NSCT coefficients of high and low frequency subbands are fused by different rules,respectively.For the high frequency subbands,the fusion rules are also unalike in the smooth and edge regions.The two regions are segregated in the panchromatic image,and the segmentation is based on particle swarm optimization.Finally,the fusion image can be obtained by performing inverse NSCT and inverse IHS transform.The experimental results are evaluated by both subjective and objective criteria.It is shown that the proposed method can obtain superior results to others.
基金Supported by projects of Institute of Geology,Chinese Academy of Geological Sciences(No.DD20160121)the National Key Research and Development Program of China(No.2020YFA0714103).
文摘The precision of Aster data is higher than that of Landsat series of multispectral remote sensing data,which can more accurately reveal the distribution of altered minerals.It plays an important role in prospecting,but it is rarely used in areas with complex terrain and high vegetation coverage.Based on this purpose,this study used Aster remote sensing data,and took Gongchangling iron deposit as a case study.It combined the mineral spectrum theory and the basic geologic data of the study area,using the model of principal component analysis(PCA)and color synthesis to extract abnormal altered minerals.The results show that the distribution of identified anomalies is basically consistent with the existing geological data in this study area,which provides a reliable reference for the mineral resources ex-ploration and delineation of mining areas.
基金This work was supported by the Project of Shandong Province Higher Educational Science and Technology Program[KJ2018BAN047,Geng,L.]National Natural Science Foundation of China[61801222,Fu,P.]+2 种基金Fundamental Research Funds for the Central Universities[30919011230,Fu,P.]Science and Technology Innovation Program for Distributed Young Talents of Shandong Province Higher Education Institutions[2019KJN045,Guo,Q.]Shandong Provincial Key Laboratory of Network Based Intelligent Computing[http://nbic.ujn.edu.cn/].
文摘The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To improve the accuracy of core tensor coding,the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in image.When applied to MS-RSI restoration,our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.
