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
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
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
[ 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 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。
