Based on the study of phase angle and wavelength in pBRDF (Polarized bidirectional reflectance distribution function), roujean model was proposed to describe Orient (Polarization phase angle) quantitatively. The Rouje...Based on the study of phase angle and wavelength in pBRDF (Polarized bidirectional reflectance distribution function), roujean model was proposed to describe Orient (Polarization phase angle) quantitatively. The Roujean model was used to quantitatively describe different fruits intensity components (<i><span style="font-family:Verdana;font-size:12px;">F</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;">) and polarization phase angle (Orient), and the simulation results were analyzed and compared using statistical analysis and comparison methods to realize the prediction from the regular model to the outdoor fruit tree canopy to the canopy of outdoor fruit tree canopy random distribution. The experimental results showed that: 1) when the phase angle of jujube was 52.19<span style="white-space:nowrap;">°</span>, 66.51<span style="white-space:nowrap;">°</span></span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">and 88.26<span style="white-space:nowrap;">°</span>, the </span><i><span style="font-family:Verdana;font-size:12px;">R</span></i><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average errors of </span><i><span style="font-family:Verdana;font-size:12px;">F</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;"> parameters described by Roujean model are 0.9982, 0.9963, 0.9912 and 3.80%, 4.17%, 6.40%, respectively;</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">a</span><span style="font-family:Verdana;font-size:12px;">nd the </span><i><span style="font-family:Verdana;font-size:12px;">R</span></i><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average error of Orient parameters described by Roujean model are 0.9056,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">0.9223,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">0.9260 and 6.23%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">3.32%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">8.05%, respectively;It can be seen that roujean model can quantitatively describe the Orient parameter of jujube</span><span style="font-family:Verdana;font-size:12px;">;</span><span style="font-family:Verdana;font-size:12px;">2) When the phase angle of apricot was 70.99<span style="white-space:nowrap;">°</span>, 71.28<span style="white-space:nowrap;">°</span> and 67.91<span style="white-space:nowrap;">°</span>, the </span><i><span style="font-family:Verdana;font-size:12px;">R</span></i><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average errors of </span><i><span style="font-family:Verdana;font-size:12px;">F</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;"> parameters described by Roujean model </span><span style="font-family:Verdana;font-size:12px;">is</span><span style="font-family:Verdana;font-size:12px;"> 0.9862, 0.9823, 0.9792 and 3.40%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">4.82%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">5.19%, respectively;</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">And the R</span><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average error of Orient parameters described by Roujean model are 0.9382, 0.8947, 0.8849 and 7.19%, 9.28%, 9.47%, respectively.</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">Roujean model can also quantitatively describe the Orient parameter of white apricot. In summary, the Roujean model can provide a good quantitative description of </span><i><span style="font-family:Verdana;font-size:12px;">f</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;"> and a good quantitative description of Orient, which in turn can predict the pBRDF parameter for more fruits with different incidence and detection directions.</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">It can correct the influence of angle factor in the nondestructive testing of outdoor fruits.</span>展开更多
Learning-based approaches have made substantial progress in capturing spatially-varying bidirectional reflectance distribution functions(SVBRDFs)from a single image with unknown lighting and geometry.However,most exis...Learning-based approaches have made substantial progress in capturing spatially-varying bidirectional reflectance distribution functions(SVBRDFs)from a single image with unknown lighting and geometry.However,most existing networks only consider per-pixel losses which limit their capability to recover local features such as smooth glossy regions.A few generative adversarial networks use multiple discriminators for different parameter maps,increasing network complexity.We present a novel end-to-end generative adversarial network(GAN)to recover appearance from a single picture of a nearly-flat surface lit by flash.We use a single unified adversarial framework for each parameter map.An attention module guides the network to focus on details of the maps.Furthermore,the SVBRDF map loss is combined to prevent paying excess attention to specular highlights.We demonstrate and evaluate our method on both public datasets and real data.Quantitative analysis and visual comparisons indicate that our method achieves better results than the state-of-the-art in most cases.展开更多
For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky sa...For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky samples makes the derived vegetation index dependent on multiple days of observations. High-frequency observations from the geostationary Fengyun(FY) satellites can significantly reduce the influence of clouds on the synthesis of terrestrial normalized difference vegetation index(NDVI). In this study, we derived the land surface vegetation index based on observational data from the Advanced Geostationary Radiation Imager(AGRI) onboard the FY-4B geostationary satellite. First, the AGRI reflectance of visible band and near-infrared band is corrected to the land surface reflectance by the 6S radiative transfer model. The bidirectional reflectance distribution function(BRDF) model is then used to normalize the AGRI surface reflectance at different observation angles and solar geometries, and an angle-independent reflectance is derived. The AGRI surface reflectance is further corrected to the MODIS levels according to the AGRI spectral response function(SRF). Finally, the daily AGRI data are used to synthesize the surface vegetation index. It is shown that the spatial distribution of NDVI images retrieved by single-day AGRI is consistent with that of 16-day MODIS data. At the same time, the dynamic range of the revised NDVI is closer to that of MODIS.展开更多
Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched An...Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.展开更多
Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysi...Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.展开更多
文摘Based on the study of phase angle and wavelength in pBRDF (Polarized bidirectional reflectance distribution function), roujean model was proposed to describe Orient (Polarization phase angle) quantitatively. The Roujean model was used to quantitatively describe different fruits intensity components (<i><span style="font-family:Verdana;font-size:12px;">F</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;">) and polarization phase angle (Orient), and the simulation results were analyzed and compared using statistical analysis and comparison methods to realize the prediction from the regular model to the outdoor fruit tree canopy to the canopy of outdoor fruit tree canopy random distribution. The experimental results showed that: 1) when the phase angle of jujube was 52.19<span style="white-space:nowrap;">°</span>, 66.51<span style="white-space:nowrap;">°</span></span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">and 88.26<span style="white-space:nowrap;">°</span>, the </span><i><span style="font-family:Verdana;font-size:12px;">R</span></i><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average errors of </span><i><span style="font-family:Verdana;font-size:12px;">F</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;"> parameters described by Roujean model are 0.9982, 0.9963, 0.9912 and 3.80%, 4.17%, 6.40%, respectively;</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">a</span><span style="font-family:Verdana;font-size:12px;">nd the </span><i><span style="font-family:Verdana;font-size:12px;">R</span></i><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average error of Orient parameters described by Roujean model are 0.9056,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">0.9223,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">0.9260 and 6.23%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">3.32%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">8.05%, respectively;It can be seen that roujean model can quantitatively describe the Orient parameter of jujube</span><span style="font-family:Verdana;font-size:12px;">;</span><span style="font-family:Verdana;font-size:12px;">2) When the phase angle of apricot was 70.99<span style="white-space:nowrap;">°</span>, 71.28<span style="white-space:nowrap;">°</span> and 67.91<span style="white-space:nowrap;">°</span>, the </span><i><span style="font-family:Verdana;font-size:12px;">R</span></i><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average errors of </span><i><span style="font-family:Verdana;font-size:12px;">F</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;"> parameters described by Roujean model </span><span style="font-family:Verdana;font-size:12px;">is</span><span style="font-family:Verdana;font-size:12px;"> 0.9862, 0.9823, 0.9792 and 3.40%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">4.82%,</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">5.19%, respectively;</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">And the R</span><sup><span style="font-family:Verdana;font-size:12px;vertical-align:super;">2</span></sup><span style="font-family:Verdana;font-size:12px;"> and average error of Orient parameters described by Roujean model are 0.9382, 0.8947, 0.8849 and 7.19%, 9.28%, 9.47%, respectively.</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">Roujean model can also quantitatively describe the Orient parameter of white apricot. In summary, the Roujean model can provide a good quantitative description of </span><i><span style="font-family:Verdana;font-size:12px;">f</span></i><sub><span style="font-family:Verdana;font-size:12px;vertical-align:sub;">00</span></sub><span style="font-family:Verdana;font-size:12px;"> and a good quantitative description of Orient, which in turn can predict the pBRDF parameter for more fruits with different incidence and detection directions.</span><span style="font-family:Verdana;font-size:12px;"> </span><span style="font-family:Verdana;font-size:12px;">It can correct the influence of angle factor in the nondestructive testing of outdoor fruits.</span>
基金supported by the National Natural Science Foundation of China(No.61602416)Shaoxing Science and Technology Plan Project(No.2020B41006).
文摘Learning-based approaches have made substantial progress in capturing spatially-varying bidirectional reflectance distribution functions(SVBRDFs)from a single image with unknown lighting and geometry.However,most existing networks only consider per-pixel losses which limit their capability to recover local features such as smooth glossy regions.A few generative adversarial networks use multiple discriminators for different parameter maps,increasing network complexity.We present a novel end-to-end generative adversarial network(GAN)to recover appearance from a single picture of a nearly-flat surface lit by flash.We use a single unified adversarial framework for each parameter map.An attention module guides the network to focus on details of the maps.Furthermore,the SVBRDF map loss is combined to prevent paying excess attention to specular highlights.We demonstrate and evaluate our method on both public datasets and real data.Quantitative analysis and visual comparisons indicate that our method achieves better results than the state-of-the-art in most cases.
基金Supported by the National Key Research and Development Program of China (2021YFB3900400)National Natural Science Foundation of China (U2142212 and U2242211)。
文摘For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky samples makes the derived vegetation index dependent on multiple days of observations. High-frequency observations from the geostationary Fengyun(FY) satellites can significantly reduce the influence of clouds on the synthesis of terrestrial normalized difference vegetation index(NDVI). In this study, we derived the land surface vegetation index based on observational data from the Advanced Geostationary Radiation Imager(AGRI) onboard the FY-4B geostationary satellite. First, the AGRI reflectance of visible band and near-infrared band is corrected to the land surface reflectance by the 6S radiative transfer model. The bidirectional reflectance distribution function(BRDF) model is then used to normalize the AGRI surface reflectance at different observation angles and solar geometries, and an angle-independent reflectance is derived. The AGRI surface reflectance is further corrected to the MODIS levels according to the AGRI spectral response function(SRF). Finally, the daily AGRI data are used to synthesize the surface vegetation index. It is shown that the spatial distribution of NDVI images retrieved by single-day AGRI is consistent with that of 16-day MODIS data. At the same time, the dynamic range of the revised NDVI is closer to that of MODIS.
文摘Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.
基金ASAP 16 project call,project title:SemantiX-A cross-sensor semantic EO data cube to open and leverage essential climate variables with scientists and the public,Grant ID:878939ASAP 17 project call,project title:SIMS-Soil sealing identification and monitoring system,Grant ID:885365.
文摘Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.
