A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target infor...A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.展开更多
A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information...A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.展开更多
Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency p...Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency parsersare limited by language and grammatical constraints. Therefore, in this work, asequential pattern-based rule mining model, which does not have such constraints,is proposed for cross-domain opinion target extraction from product reviews inunknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also revealsthe difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the firststage, the aspects of reviews are extracted from the target domain using the rulesautomatically generated from source domains. The aspects are also transferredfrom the source domains to a target domain. Moreover, aspect pruning is appliedto further improve the performance of aspect extraction. In the second stage, theopinion target is extracted among the aspects extracted at the former stage usingthe rules automatically generated for opinion target extraction. The proposedmodel was evaluated on several benchmark datasets in different domains andcompared against the literature. The experimental results revealed that the opiniontargets of the reviews in unknown domains can be extracted with higher accuracythan those of the previous works.展开更多
Computing resources are one of the key factors restricting the extraction of marine targets by using deep learning.In order to increase computing speed and shorten the computing time,parallel distributed architecture ...Computing resources are one of the key factors restricting the extraction of marine targets by using deep learning.In order to increase computing speed and shorten the computing time,parallel distributed architecture is adopted to extract marine targets.The advantages of two distributed architectures,Parameter Server and Ring-allreduce architecture,are combined to design a parallel distributed architecture suitable for deep learning–Optimal Interleaved Distributed Architecture(OIDA).Three marine target extraction methods including OTD_StErf,OTD_Loglogistic and OTD_Sgmloglog are used to test OIDA,and a total of 18 experiments in 3categories are carried out.The results show that OIDA architecture can meet the timeliness requirements of marine target extraction.The average speed of target parallel extraction with single-machine 8-core CPU is 5.75 times faster than that of single-machine single-core CPU,and the average speed with 5-machine 40-core CPU is 20.75 times faster.展开更多
It is difficult to extract targets under strong environmental disturbance in practice.Ghost imaging(GI)is an innovative antiinterference imaging technology.In this paper,we propose a scheme for target extraction based...It is difficult to extract targets under strong environmental disturbance in practice.Ghost imaging(GI)is an innovative antiinterference imaging technology.In this paper,we propose a scheme for target extraction based on characteristicenhanced pseudo-thermal GI.Unlike traditional GI which relies on training the detected signals or imaging results,our scheme trains the illuminating light fields using a deep learning network to enhance the target’s characteristic response.The simulation and experimental results prove that our imaging scheme is sufficient to perform single-and multiple-target extraction at low measurements.In addition,the effect of a strong scattering environment is discussed,and the results show that the scattering disturbance hardly affects the target extraction effect.The proposed scheme presents the potential application in target extraction through scattering media.展开更多
[ Objective] The study aimed to supply important basis for developing environment-friendly pesticides with myricetin and crude extract of Myrica rubra leaves as effective components. [ Method] According to ＂Test guid...[ Objective] The study aimed to supply important basis for developing environment-friendly pesticides with myricetin and crude extract of Myrica rubra leaves as effective components. [ Method] According to ＂Test guidelines for environmental safety evaluation on chemical pesticides＂, the toxicity of myricetin and crude extract of M. rubra leaves on non-target organisms was determined and the safety evaluation was carried out. [Result] MyriceUn and crude extract of M. rubra leaves had low toxicity on non-target organisms, such as earthworm, silkworm and soil microbes. Myricetin showed low toxicity and the crude extract of M. rubra leaves showed middle toxicity on tadpole. The high-concentration crude extract of M. rubra leaves had some antifeedant effect on silkworm. [ Conclusion] Myricetin and crude extract of M. rubra leaves had low toxicity on non-tar- get organisms in environment and they were relatively safe.展开更多
This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images...This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images for the real data compared with the simulated ISAR images are analyzed firstly. Then,the novel technique for the target recognition is proposed,and it consists of three steps,including the preprocessing,feature extraction and classification. Some segmentation and morphological methods are used in the preprocessing to obtain the clear target images. Then,six different features for the ISAR images are extracted.By estimating the features' conditional probability, the effectiveness and robustness of these features are demonstrated. Finally,Fisher's linear classifier is applied in the classification step. The results for the allfeature space are provided to illustrate the effectiveness of the proposed method.展开更多
Elastic acoustic scattering is important for buried target detection and identification. For elastic spherical objects, studies have shown that a series of narrowband energetic arrivals follow the first specular one. ...Elastic acoustic scattering is important for buried target detection and identification. For elastic spherical objects, studies have shown that a series of narrowband energetic arrivals follow the first specular one. However, in practice, the elastic echo is rather weak because of the acoustic absorption, propagation loss, and reverberation, which makes it difficult to extract elastic scattering features, especially for buried targets. To remove the interference and enhance the elastic scattering, the de-chirping method was adopted here to address the target scattering echo when a linear frequency modulation (LFM) signal is transmitted. The parameters of the incident signal were known. With the de-chirping operation, a target echo was transformed into a cluster of narrowband signals, and the elastic components could be extracted with a band-pass filter and then recovered by remodulation. The simulation results indicate the feasibility of the elastic scattering extraction and recovery. The experimental result demonstrates that the interference was removed and the elastic scattering was visibly enhanced after de-chirping, which facilitates the subsequent resonance feature extraction for target classification and recognition.展开更多
针对玉米杂草识别过程中因光照变化导致识别精确度低及漏检问题,该研究以幼苗期玉米及其伴生杂草为研究对象,设计一种基于WEED-YOLOv10的玉米杂草检测方法。首先,通过无人机快速采集田间高分辨率图像构建了玉米杂草数据集;其次,以YOLOv...针对玉米杂草识别过程中因光照变化导致识别精确度低及漏检问题,该研究以幼苗期玉米及其伴生杂草为研究对象,设计一种基于WEED-YOLOv10的玉米杂草检测方法。首先,通过无人机快速采集田间高分辨率图像构建了玉米杂草数据集;其次,以YOLOv10n为基线网络,将骨干网络替换为ConvNeXtV2以增强特征提取能力;继而,为避免因模块拼接可能带来的信息冗余或丢失问题提升对光照干扰的鲁棒性,嵌入CBAM注意力机制;然后,引入SlimNeck结构优化网络计算效率,有效平衡了模型计算资源消耗与特征表征能力;最后,使用Focaler-EIoU损失函数进一步提高模型定位精度。试验结果表明,WEED-YOLOv10在精确率、召回率、mAP@50、mAP@50:95和F1分数上分别达到85.4%、88.1%、90.9%、48.5%和86.7%,较基准模型分别提升了2.4、2.9、3.5、7.0、2.6个百分点,各项精度指标均优于其他对比模型,部署在NVIDIA Jetson orin NX上的图片推理速度达到28.7帧/s,实现了检测速度与精度的平衡。进一步地,基于WEED-YOLOv10开发对靶喷药系统,该系统实时捕捉并解析来自模型的识别信号,实现对除草喷施装置的精准调控。田间试验结果显示,对靶喷药系统施药准确率为93.7%,喷洒覆盖率为90.5%,对靶偏差为1.45cm,杂草实时检测速度为20.1帧/s,实现了自动化的玉米田间除草作业。该研究为复杂光照场景下农田杂草治理提供了可靠的技术方案,对推动农业智能化作业具有重要意义。展开更多
The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of unde...The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.展开更多
An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by o...An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by one, and neural network algorithm. The techniques include vehicles sensing, image garb control, vehicle license location, lighting and optic character recognition. The system is much more robust and faster than the traditional thresholding method.展开更多
Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for gro...Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.展开更多
Two new systems have been presented for the extraction separation of <sup>68</sup>Ga from irradiated Ga<sub>2</sub>O<sub>3</sub> target after proton bombardment. It could avoid the ...Two new systems have been presented for the extraction separation of <sup>68</sup>Ga from irradiated Ga<sub>2</sub>O<sub>3</sub> target after proton bombardment. It could avoid the loss of <sup>68</sup>GeCl<sub>4</sub> during the processing and storage, resulting a stable <sup>68</sup>Ge source.展开更多
With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on ...With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.展开更多
Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spec...Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.展开更多
针对遥感图像微小目标检测中存在的浅层细化特征、深层语义表征和多尺度信息提取3个问题,提出一种综合运用多项技术的跨尺度YOLOv7(cross-scale YOLOv7,CSYOLOv7)网络。首先,设计跨阶段特征提取模块(cross-stage feature extraction mod...针对遥感图像微小目标检测中存在的浅层细化特征、深层语义表征和多尺度信息提取3个问题,提出一种综合运用多项技术的跨尺度YOLOv7(cross-scale YOLOv7,CSYOLOv7)网络。首先,设计跨阶段特征提取模块(cross-stage feature extraction module,CFEM)和感受野特征增强模块(receptive field feature enhancement module,RFFEM)。CFEM提高模型细化特征提取能力并抑制浅层下采样过程中特征的丢失,RFFEM加大网络对深层语义特征的提取力度,增强模型对目标上下文信息获取能力。其次,设计跨梯度空间金字塔池化模块(cross-gradient space pyramid pool module,CSPPM)有效融合微小目标多尺度的全局和局部特征。最后,用形状感知交并比(shape-aware intersection over union,Shape IoU)替换完全交并比(complete intersection over union,CIoU),提高模型在边界框定位任务中的精确度。实验结果表明,CSYOLOv7网络在DIOR(dataset for image object recognition)数据集和NWPU VHR-10(Northwestern Polytechnical University Very High Resolution-10)数据集上分别取得了74%和89.6%的检测精度,有效提升遥感图像微小目标的检测效果。展开更多
基金Funded by the National 863 Program of China (No.2002AA783050)
文摘A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.
基金the National Natural Science Foundation of China (No. 60675023)the Aviation Science Foundation of China (No. 04F57004)
文摘A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.
文摘Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency parsersare limited by language and grammatical constraints. Therefore, in this work, asequential pattern-based rule mining model, which does not have such constraints,is proposed for cross-domain opinion target extraction from product reviews inunknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also revealsthe difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the firststage, the aspects of reviews are extracted from the target domain using the rulesautomatically generated from source domains. The aspects are also transferredfrom the source domains to a target domain. Moreover, aspect pruning is appliedto further improve the performance of aspect extraction. In the second stage, theopinion target is extracted among the aspects extracted at the former stage usingthe rules automatically generated for opinion target extraction. The proposedmodel was evaluated on several benchmark datasets in different domains andcompared against the literature. The experimental results revealed that the opiniontargets of the reviews in unknown domains can be extracted with higher accuracythan those of the previous works.
基金the Natural Science Foundation of Shandong Province(No.ZR2019MD034)the Education Reform Project of Shandong Province(No.M2020266)。
文摘Computing resources are one of the key factors restricting the extraction of marine targets by using deep learning.In order to increase computing speed and shorten the computing time,parallel distributed architecture is adopted to extract marine targets.The advantages of two distributed architectures,Parameter Server and Ring-allreduce architecture,are combined to design a parallel distributed architecture suitable for deep learning–Optimal Interleaved Distributed Architecture(OIDA).Three marine target extraction methods including OTD_StErf,OTD_Loglogistic and OTD_Sgmloglog are used to test OIDA,and a total of 18 experiments in 3categories are carried out.The results show that OIDA architecture can meet the timeliness requirements of marine target extraction.The average speed of target parallel extraction with single-machine 8-core CPU is 5.75 times faster than that of single-machine single-core CPU,and the average speed with 5-machine 40-core CPU is 20.75 times faster.
基金supported by the National Natural Science Foundation of China(Nos.61971184,62001162,62101187)the Hunan Provincial Natural Science Foundation(No.2022JJ40091)the Fundamental Research Funds for the Central Universities(No.531118010757)。
文摘It is difficult to extract targets under strong environmental disturbance in practice.Ghost imaging(GI)is an innovative antiinterference imaging technology.In this paper,we propose a scheme for target extraction based on characteristicenhanced pseudo-thermal GI.Unlike traditional GI which relies on training the detected signals or imaging results,our scheme trains the illuminating light fields using a deep learning network to enhance the target’s characteristic response.The simulation and experimental results prove that our imaging scheme is sufficient to perform single-and multiple-target extraction at low measurements.In addition,the effect of a strong scattering environment is discussed,and the results show that the scattering disturbance hardly affects the target extraction effect.The proposed scheme presents the potential application in target extraction through scattering media.
基金Supported by Major Program in Zhejiang Science and Technology Department(2008C22G2100038)Emerging Artists Project in Zhejiang Province(21000037)~~
文摘[ Objective] The study aimed to supply important basis for developing environment-friendly pesticides with myricetin and crude extract of Myrica rubra leaves as effective components. [ Method] According to ＂Test guidelines for environmental safety evaluation on chemical pesticides＂, the toxicity of myricetin and crude extract of M. rubra leaves on non-target organisms was determined and the safety evaluation was carried out. [Result] MyriceUn and crude extract of M. rubra leaves had low toxicity on non-target organisms, such as earthworm, silkworm and soil microbes. Myricetin showed low toxicity and the crude extract of M. rubra leaves showed middle toxicity on tadpole. The high-concentration crude extract of M. rubra leaves had some antifeedant effect on silkworm. [ Conclusion] Myricetin and crude extract of M. rubra leaves had low toxicity on non-tar- get organisms in environment and they were relatively safe.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.61622107 and 61471149)
文摘This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images for the real data compared with the simulated ISAR images are analyzed firstly. Then,the novel technique for the target recognition is proposed,and it consists of three steps,including the preprocessing,feature extraction and classification. Some segmentation and morphological methods are used in the preprocessing to obtain the clear target images. Then,six different features for the ISAR images are extracted.By estimating the features' conditional probability, the effectiveness and robustness of these features are demonstrated. Finally,Fisher's linear classifier is applied in the classification step. The results for the allfeature space are provided to illustrate the effectiveness of the proposed method.
文摘Elastic acoustic scattering is important for buried target detection and identification. For elastic spherical objects, studies have shown that a series of narrowband energetic arrivals follow the first specular one. However, in practice, the elastic echo is rather weak because of the acoustic absorption, propagation loss, and reverberation, which makes it difficult to extract elastic scattering features, especially for buried targets. To remove the interference and enhance the elastic scattering, the de-chirping method was adopted here to address the target scattering echo when a linear frequency modulation (LFM) signal is transmitted. The parameters of the incident signal were known. With the de-chirping operation, a target echo was transformed into a cluster of narrowband signals, and the elastic components could be extracted with a band-pass filter and then recovered by remodulation. The simulation results indicate the feasibility of the elastic scattering extraction and recovery. The experimental result demonstrates that the interference was removed and the elastic scattering was visibly enhanced after de-chirping, which facilitates the subsequent resonance feature extraction for target classification and recognition.
文摘针对玉米杂草识别过程中因光照变化导致识别精确度低及漏检问题,该研究以幼苗期玉米及其伴生杂草为研究对象,设计一种基于WEED-YOLOv10的玉米杂草检测方法。首先,通过无人机快速采集田间高分辨率图像构建了玉米杂草数据集;其次,以YOLOv10n为基线网络,将骨干网络替换为ConvNeXtV2以增强特征提取能力;继而,为避免因模块拼接可能带来的信息冗余或丢失问题提升对光照干扰的鲁棒性,嵌入CBAM注意力机制;然后,引入SlimNeck结构优化网络计算效率,有效平衡了模型计算资源消耗与特征表征能力;最后,使用Focaler-EIoU损失函数进一步提高模型定位精度。试验结果表明,WEED-YOLOv10在精确率、召回率、mAP@50、mAP@50:95和F1分数上分别达到85.4%、88.1%、90.9%、48.5%和86.7%,较基准模型分别提升了2.4、2.9、3.5、7.0、2.6个百分点,各项精度指标均优于其他对比模型,部署在NVIDIA Jetson orin NX上的图片推理速度达到28.7帧/s,实现了检测速度与精度的平衡。进一步地,基于WEED-YOLOv10开发对靶喷药系统,该系统实时捕捉并解析来自模型的识别信号,实现对除草喷施装置的精准调控。田间试验结果显示,对靶喷药系统施药准确率为93.7%,喷洒覆盖率为90.5%,对靶偏差为1.45cm,杂草实时检测速度为20.1帧/s,实现了自动化的玉米田间除草作业。该研究为复杂光照场景下农田杂草治理提供了可靠的技术方案,对推动农业智能化作业具有重要意义。
基金Supported by project of Natural Science Foundation of China(No.41174097)
文摘The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.
基金Supported by the Emphases Science and Technology Tackle Key Problem of Wuhan!(98320 1005)
文摘An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by one, and neural network algorithm. The techniques include vehicles sensing, image garb control, vehicle license location, lighting and optic character recognition. The system is much more robust and faster than the traditional thresholding method.
文摘Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.
文摘Two new systems have been presented for the extraction separation of <sup>68</sup>Ga from irradiated Ga<sub>2</sub>O<sub>3</sub> target after proton bombardment. It could avoid the loss of <sup>68</sup>GeCl<sub>4</sub> during the processing and storage, resulting a stable <sup>68</sup>Ge source.
基金supported by the National Natural Science Foundation of China(61471191)the Aeronautical Science Foundation of China(20152052026)the Foundation of Graduate Innovation Center in NUAA(kfjj20170313)
文摘With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.
文摘Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.
文摘针对遥感图像微小目标检测中存在的浅层细化特征、深层语义表征和多尺度信息提取3个问题,提出一种综合运用多项技术的跨尺度YOLOv7(cross-scale YOLOv7,CSYOLOv7)网络。首先,设计跨阶段特征提取模块(cross-stage feature extraction module,CFEM)和感受野特征增强模块(receptive field feature enhancement module,RFFEM)。CFEM提高模型细化特征提取能力并抑制浅层下采样过程中特征的丢失,RFFEM加大网络对深层语义特征的提取力度,增强模型对目标上下文信息获取能力。其次,设计跨梯度空间金字塔池化模块(cross-gradient space pyramid pool module,CSPPM)有效融合微小目标多尺度的全局和局部特征。最后,用形状感知交并比(shape-aware intersection over union,Shape IoU)替换完全交并比(complete intersection over union,CIoU),提高模型在边界框定位任务中的精确度。实验结果表明,CSYOLOv7网络在DIOR(dataset for image object recognition)数据集和NWPU VHR-10(Northwestern Polytechnical University Very High Resolution-10)数据集上分别取得了74%和89.6%的检测精度,有效提升遥感图像微小目标的检测效果。
