Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or select...Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.展开更多
We consider generalizations of the Radon-Schmid transform on coherent DG/H-Modules, with the intention of obtaining the equivalence between geometric objects (vector bundles) and algebraic objects (D-Modules) characte...We consider generalizations of the Radon-Schmid transform on coherent DG/H-Modules, with the intention of obtaining the equivalence between geometric objects (vector bundles) and algebraic objects (D-Modules) characterizing conformal classes in the space-time that determine a space moduli [1] on coherent sheaves for the securing solutions in field theory [2]. In a major context, elements of derived categories like D-branes and heterotic strings are considered, and using the geometric Langlands program, a moduli space is obtained of equivalence between certain geometrical pictures (non-conformal world sheets [3]) and physical stacks (derived sheaves), that establishes equivalence between certain theories of super symmetries of field of a Penrose transform that generalizes the implications given by the Langlands program. With it we obtain extensions of a cohomology of integrals for a major class of field equations to corresponding Hecke category.展开更多
为探明露场叠盘暗化出苗(darkened emergence by stacked plates in open space,DE)与无纺布覆盖(seedling mulched with non-woven fabric,NM)协同育秧对机插杂交籼稻成苗特性的影响,该研究于2020—2021年在不同气候条件下以杂交籼稻...为探明露场叠盘暗化出苗(darkened emergence by stacked plates in open space,DE)与无纺布覆盖(seedling mulched with non-woven fabric,NM)协同育秧对机插杂交籼稻成苗特性的影响,该研究于2020—2021年在不同气候条件下以杂交籼稻天优华占为材料,以摆盘出苗(swing-plate emergence,SE)和薄膜覆盖(seedling mulched with film,FM)为对照进行试验,探究DE与NM对出苗微环境与出苗特征以及秧苗素质的影响,并验证其机插适应性。结果表明:1)相对于外界环境,DE提高了出苗微环境的平均温度与平均相对湿度。与SE相比,DE降低了温度与相对湿度变异系数,缩短了种子出苗时间,并大幅提高出苗率与出苗整齐度;较FM,NM改善了秧床微环境,促进秧苗均衡生长;DE+NM有效提高成苗整齐度与成苗率,2 a间成苗整齐度较DE+FM、SE+NM与SE+FM分别增加了7.22~8.91、3.67~5.55和8.29~10.38个百分点(P<0.05),2 a间成苗率分别提高了4.08~8.09、11.79~14.88和20.10~40.02个百分点(除2021年DE+FM外,P<0.05)。2)DE的成苗密度显著优于SE(P<0.05),进而增加了单位面积的总根长、白根数、根表面积,及根系盘结力;与FM相比,NM显著增大了单位面积根体积(P<0.05);DE+NM增加了单位面积的根体积、根表面积、根生物量,同时提高了根系盘结力(除2020年DE+FM外)。3)较其他3种育秧处理,DE+NM显著降低漏插率与伤秧率(P<0.05),提高3~4苗/穴比例,并增加了每穴苗数。综上所述,DE+NM通过改善育秧环境实现了培育壮秧,秧苗机插效果好,所育秧苗呈现出“齐苗、壮根、宜机插”特性。该育秧技术有效解决了四川稻区传统机插育秧存在的秧苗生长不齐、盘根弱、机插效果差等问题,并兼具低成本、耐粗放、高效低碳等特点。研究结果可为本区域机插壮秧培育和水稻丰产增效栽培提供理论与技术支撑。展开更多
基金supported by the National Natural Science Foundation of China (67441830108 and 41871224)。
文摘Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.
文摘We consider generalizations of the Radon-Schmid transform on coherent DG/H-Modules, with the intention of obtaining the equivalence between geometric objects (vector bundles) and algebraic objects (D-Modules) characterizing conformal classes in the space-time that determine a space moduli [1] on coherent sheaves for the securing solutions in field theory [2]. In a major context, elements of derived categories like D-branes and heterotic strings are considered, and using the geometric Langlands program, a moduli space is obtained of equivalence between certain geometrical pictures (non-conformal world sheets [3]) and physical stacks (derived sheaves), that establishes equivalence between certain theories of super symmetries of field of a Penrose transform that generalizes the implications given by the Langlands program. With it we obtain extensions of a cohomology of integrals for a major class of field equations to corresponding Hecke category.
文摘为探明露场叠盘暗化出苗(darkened emergence by stacked plates in open space,DE)与无纺布覆盖(seedling mulched with non-woven fabric,NM)协同育秧对机插杂交籼稻成苗特性的影响,该研究于2020—2021年在不同气候条件下以杂交籼稻天优华占为材料,以摆盘出苗(swing-plate emergence,SE)和薄膜覆盖(seedling mulched with film,FM)为对照进行试验,探究DE与NM对出苗微环境与出苗特征以及秧苗素质的影响,并验证其机插适应性。结果表明:1)相对于外界环境,DE提高了出苗微环境的平均温度与平均相对湿度。与SE相比,DE降低了温度与相对湿度变异系数,缩短了种子出苗时间,并大幅提高出苗率与出苗整齐度;较FM,NM改善了秧床微环境,促进秧苗均衡生长;DE+NM有效提高成苗整齐度与成苗率,2 a间成苗整齐度较DE+FM、SE+NM与SE+FM分别增加了7.22~8.91、3.67~5.55和8.29~10.38个百分点(P<0.05),2 a间成苗率分别提高了4.08~8.09、11.79~14.88和20.10~40.02个百分点(除2021年DE+FM外,P<0.05)。2)DE的成苗密度显著优于SE(P<0.05),进而增加了单位面积的总根长、白根数、根表面积,及根系盘结力;与FM相比,NM显著增大了单位面积根体积(P<0.05);DE+NM增加了单位面积的根体积、根表面积、根生物量,同时提高了根系盘结力(除2020年DE+FM外)。3)较其他3种育秧处理,DE+NM显著降低漏插率与伤秧率(P<0.05),提高3~4苗/穴比例,并增加了每穴苗数。综上所述,DE+NM通过改善育秧环境实现了培育壮秧,秧苗机插效果好,所育秧苗呈现出“齐苗、壮根、宜机插”特性。该育秧技术有效解决了四川稻区传统机插育秧存在的秧苗生长不齐、盘根弱、机插效果差等问题,并兼具低成本、耐粗放、高效低碳等特点。研究结果可为本区域机插壮秧培育和水稻丰产增效栽培提供理论与技术支撑。
