Estimating cross-range velocity is a challenging task for space-borne synthetic aperture radar（SAR）, which is important for ground moving target indication（GMTI）. Because the velocity of a target is very small com...Estimating cross-range velocity is a challenging task for space-borne synthetic aperture radar（SAR）, which is important for ground moving target indication（GMTI）. Because the velocity of a target is very small compared with that of the satellite, it is difficult to correctly estimate it using a conventional monostatic platform algorithm. To overcome this problem, a novel method employing multistatic SAR is presented in this letter. The proposed hybrid method, which is based on an extended space-time model（ESTIM） of the azimuth signal, has two steps： first, a set of finite impulse response（FIR） filter banks based on a fractional Fourier transform（FrFT） is used to separate multiple targets within a range gate; second, a cross-correlation spectrum weighted subspace fitting（CSWSF） algorithm is applied to each of the separated signals in order to estimate their respective parameters. As verified through computer simulation with the constellations of Cartwheel, Pendulum and Helix, this proposed time-frequency-subspace method effectively improves the estimation precision of the cross-range velocities of multiple targets.展开更多
Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health,weed presence and phenologi...Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health,weed presence and phenological state,among others.Traditionally,models based on normalized difference vegetation index(NDVI),near infrared channel(NIR)or RGB have been a good indicator of vegetation presence.However,these methods are not suitable for accurately segmenting vegetation showing damage,which precludes their use for downstream phenotyping algorithms.In this paper,we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation.The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image.Second,we compute two newly proposed vegetation indices from this estimated virtual NIR:the infrared-dark channel subtraction(IDCS)and infrared-dark channel ratio(IDCR)indices.Finally,both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition.The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days.The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel(F1=0:94)and with the proposed IDCR and IDCS vegetation indices(F1=0:95)derived from the estimated NIR channel,while the use of only the image or RGB indices lead to inferior performance(RGB(F1=0:90)NIR(F1=0:82)or NDVI(F1=0:89)channel).The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions.展开更多
To the editor,We read with interest the recent article by Li et al[1],published in Intelligent Medicine,which proposes a machine learning model to estimate corneal stiffness indicators and detect keratoconus without r...To the editor,We read with interest the recent article by Li et al[1],published in Intelligent Medicine,which proposes a machine learning model to estimate corneal stiffness indicators and detect keratoconus without relying on biomechanical equipment.The use of ensemble models—combining random forests with other classifiers—has demonstrated improved predictive performance,yielding a higher area under the receiver operating characteristic curve(AUC),an enhanced combined corneal biomechanical index(cCBI),superior intraclass correlation coefficient(ICC)values,and more accurate stiffness parameter at first applanation(SP-A1)measurements.(e.g.,ICC=0.82 for SP-A1 and AUC=0.935 for cCBI classification).These results support the idea of using surrogate modeling to democratize access to biomechanical diagnostics.Although we appreciate the study’s clinical and computational strengths,we believe that it is crucial to examine the mathematical foundations and fairness implications that underpin such systems.展开更多
基金supported by the National Natural Science Foundation of China (No. 61271343)the Research Fund for the Doctoral Program of Higher Education of China (No. 20122302110012)the 2014 Innovation of Science and Technology Program of China Aerospace Science and Technology Corporation
文摘Estimating cross-range velocity is a challenging task for space-borne synthetic aperture radar（SAR）, which is important for ground moving target indication（GMTI）. Because the velocity of a target is very small compared with that of the satellite, it is difficult to correctly estimate it using a conventional monostatic platform algorithm. To overcome this problem, a novel method employing multistatic SAR is presented in this letter. The proposed hybrid method, which is based on an extended space-time model（ESTIM） of the azimuth signal, has two steps： first, a set of finite impulse response（FIR） filter banks based on a fractional Fourier transform（FrFT） is used to separate multiple targets within a range gate; second, a cross-correlation spectrum weighted subspace fitting（CSWSF） algorithm is applied to each of the separated signals in order to estimate their respective parameters. As verified through computer simulation with the constellations of Cartwheel, Pendulum and Helix, this proposed time-frequency-subspace method effectively improves the estimation precision of the cross-range velocities of multiple targets.
文摘Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health,weed presence and phenological state,among others.Traditionally,models based on normalized difference vegetation index(NDVI),near infrared channel(NIR)or RGB have been a good indicator of vegetation presence.However,these methods are not suitable for accurately segmenting vegetation showing damage,which precludes their use for downstream phenotyping algorithms.In this paper,we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation.The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image.Second,we compute two newly proposed vegetation indices from this estimated virtual NIR:the infrared-dark channel subtraction(IDCS)and infrared-dark channel ratio(IDCR)indices.Finally,both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition.The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days.The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel(F1=0:94)and with the proposed IDCR and IDCS vegetation indices(F1=0:95)derived from the estimated NIR channel,while the use of only the image or RGB indices lead to inferior performance(RGB(F1=0:90)NIR(F1=0:82)or NDVI(F1=0:89)channel).The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions.
文摘To the editor,We read with interest the recent article by Li et al[1],published in Intelligent Medicine,which proposes a machine learning model to estimate corneal stiffness indicators and detect keratoconus without relying on biomechanical equipment.The use of ensemble models—combining random forests with other classifiers—has demonstrated improved predictive performance,yielding a higher area under the receiver operating characteristic curve(AUC),an enhanced combined corneal biomechanical index(cCBI),superior intraclass correlation coefficient(ICC)values,and more accurate stiffness parameter at first applanation(SP-A1)measurements.(e.g.,ICC=0.82 for SP-A1 and AUC=0.935 for cCBI classification).These results support the idea of using surrogate modeling to democratize access to biomechanical diagnostics.Although we appreciate the study’s clinical and computational strengths,we believe that it is crucial to examine the mathematical foundations and fairness implications that underpin such systems.
