Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balan...Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition,the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.展开更多
High-resolution approaches such as multiple signal classification and estimation of signal parameters via rotational invariance techniques（ESPRIT） are currently employed widely in multibeam echo-sounder（MBES）syste...High-resolution approaches such as multiple signal classification and estimation of signal parameters via rotational invariance techniques（ESPRIT） are currently employed widely in multibeam echo-sounder（MBES）systems for sea floor bathymetry,where a uniform line array is also required.However,due to the requirements in terms of the system coverage/resolution and installation space constraints,an MBES system usually employs a receiving array with a special shape,which means that high-resolution algorithms cannot be applied directly.In addition,the short-term stationary echo signals make it difficult to estimate the covariance matrix required by the high-resolution approaches,which further increases the complexity when applying the high-resolution algorithms in the MBES systems.The ESPRIT with multiple-angle subarray beamforming is employed to reduce the requirements in terms of the signal-to-noise ratio,number of snapshots,and computational effort.The simulations show that the new processing method can provide better fine-structure resolution.Then a highresolution bottom detection（HRBD） algorithm is developed by combining the new processing method with virtual array transformation.The application of the HRBD algorithm to a U-shaped array is also discuss.The computer simulations and experimental data processing results verify the effectiveness of the proposed algorithm.展开更多
A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly ...A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.展开更多
Multichannel high-resolution and wide-swath(HRWS)imaging is an advanced digital beamforming technique for future synthetic aperture radar(SAR)systems.However,radio frequency interference(RFI)is a critical concern for ...Multichannel high-resolution and wide-swath(HRWS)imaging is an advanced digital beamforming technique for future synthetic aperture radar(SAR)systems.However,radio frequency interference(RFI)is a critical concern for HRWS SAR missions,which distorts measure-ments and produces image artifacts.In this paper,the spatial cross-correlation coefficients of multichannel HRWS SAR signals are investigated for RFI detection.It is found when the two channels are correlated,RFI-polluted areas present lower coherence values than non-polluted areas in the same scenarios,which makes previous methods fail.Further,this paper studies the case of two fully decorrelated channels to maximize the coherence difference among RFI and target echoes,and RFI detection is realized by exploiting the anomaly value of coherence.Experimental results of real air-borne multichannel SAR data demonstrate that the RFI can be detected successfully.展开更多
The value of the high-resolution data lies in the high-precision information discovery.The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevat...The value of the high-resolution data lies in the high-precision information discovery.The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevation models(DEMs).However,the results of landform element extraction generated by classical methods might be ungrounded on high-resolution DEMs.This paper presents our research on using the aspect to reinforce the applicability and robustness of the classical approaches in landform element extraction.First,according to the research of pattern recognition,we assume that aspect-enhanced landform representation is robust to rotation,scaling and affine variance.To testify the role of aspect,we respectively integrated the aspect into three classical approaches:mean curvaturebased fuzzy classification,elevation-based feature descriptor,and object-based segmentation.In the experiment,based on four types of high-resolution DEMs(1 m,2 m,4 m and 8 m),we compare each classical approaches and their corresponding aspect-enhanced approaches based on extracting the rims of two craters having different landforms,and the ridgelines and valleylines of a region covered by few vegetables and man-made buildings.In comparison to the results generated by curvature-based fuzzy classification,the aspect enhanced curvature-based fuzzy classification can effectively filter a number of noises outperforms the curvature-based one.Otherwise,the aspect-enhanced feature descriptor can detect more landform elements than the elevation-based feature descriptor.Moreover,the aspect-based segmentation can detect the main structure of landform,while the boundaries segmented by classical approaches are messing and meaningless.The systematic experiments on meter-level resolution DEMs proved that the aspect in topography could effectively to improve the classical method-system,including fuzzy-based classification,feature descriptors-based detection and object-based segmentation.The value of aspect is significantly great to be worthy of attentions in landform representation.展开更多
Forest is the largest carbon reservoir and carbon absorber on earth.Thus,mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal.Accurate forest change informatio...Forest is the largest carbon reservoir and carbon absorber on earth.Thus,mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal.Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images.However,deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources.Therefore,there was an urgent need for a fast and accurate deforestation detection model.In this study,we proposed an interesting but effective re-parameterization deforestation detection model,named RepDDNet.Unlike other existing models designed for deforestation detection,the main feature of RepDDNet was its decoupling feature,which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage,thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged.A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images(the total area of it was over 20,000 square kilometers),and the result indicated that the model computation efficiency could be improved by nearly 30%compared with the model without re-parameterization.Additionally,compared with other lightweight models,RepDDNet also displayed a trade-off between accuracy and computation efficiency.展开更多
Photonics-based radar with a photonic de-chirp receiver has the advantages of broadband operation and real-time signal processing, but it suffers from interference from image frequencies and other undesired frequency-...Photonics-based radar with a photonic de-chirp receiver has the advantages of broadband operation and real-time signal processing, but it suffers from interference from image frequencies and other undesired frequency-mixing components, due to single-channel real-valued photonic frequency mixing. In this paper, we propose a photonicsbased radar with a photonic frequency-doubling transmitter and a balanced in-phase and quadrature(I/Q)de-chirp receiver. This radar transmits broadband linearly frequency-modulated signals generated by photonic frequency doubling and performs I/Q de-chirping of the radar echoes based on a balanced photonic I/Q frequency mixer, which is realized by applying a 90° optical hybrid followed by balanced photodetectors. The proposed radar has a high range resolution because of the large operation bandwidth and achieves interference-free detection by suppressing the image frequencies and other undesired frequency-mixing components. In the experiment, a photonics-based K-band radar with a bandwidth of 8 GHz is demonstrated. The balanced I/Q de-chirping receiver achieves an image-rejection ratio of over 30 dB and successfully eliminates the interference due to the baseband envelope and the frequency mixing between radar echoes of different targets. In addition, the desired dechirped signal power is also enhanced with balanced detection. Based on the established photonics-based radar,inverse synthetic aperture radar imaging is also implemented, through which the advantages of the proposed radar are verified.展开更多
The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution(HR) remote sensing data and emerging of new satellite sensors, HR observation of u...The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution(HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.展开更多
In the last years,endoscopic techniques gained a crucial role in the treatment of colorectal flat lesions.At the same time,the importance of a reliable assessment of such lesions to predict the malignancy and the dept...In the last years,endoscopic techniques gained a crucial role in the treatment of colorectal flat lesions.At the same time,the importance of a reliable assessment of such lesions to predict the malignancy and the depth of invasion of the colonic wall emerged.The current unsolved dilemma about the endoscopic excision techniques concerns the necessity of a reliable submucosal invasive cancer assessment system that can stratify the risk of the post-procedural need for surgery.Accordingly,this narrative literature review aims to compare the available diagnostic strategies in predicting malignancy and to give a guide about the best techniques to employ.We performed a literature search using electronic databases(MEDLINE/PubMed,EMBASE,and Cochrane Library).We collected all articles about endoscopic mucosal resection(EMR)and endoscopic submucosal dissection(ESD)registering the outcomes.Moreover,we analyzed all meta-analyses comparing EMR vs ESD outcomes for colorectal sessile or nonpolypoid lesions of any size,preoperatively estimated as non-invasive.Seven meta-analysis studies,mainly Eastern,were included in the analysis comparing 124 studies and overall 22954 patients who underwent EMR and ESD procedures.Of these,eighty-two were retrospective,twenty-four perspective,nine casecontrol,and six cohorts,while three were randomized clinical trials.A total of 18118 EMR and 10379 ESD were completed for a whole of 28497 colorectal sessile or non-polypoid lesions>5-10 mm in size.In conclusion,it is crucial to enhance the preoperative diagnostic workup,especially in deciding the most suitable endoscopic method for radical resection of flat colorectal lesions at risk of underlying malignancy.Additionally,the ESD necessitates further improvement because of the excessively time-consuming as well as the intraprocedural technical hindrances and related complications.We found a higher rate of en bloc resections and R0 for ESD than EMR for non-pedunculated colorectal lesions.Nevertheless,despite the lower local recurrence rates,ESD had greater perforation rates and needed lengthier procedural times.The prevailing risk for additional surgery in ESD rather than EMR for complications or oncologic reasons is still uncertain.展开更多
In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to ...In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to extract building areas in ur- ban areas from very high resolution SAR images is presented. The results showed that this method has the ability to differentiate buildings from the complicated features in urban areas, which can be employed for land mapping and provides support for relief operations.展开更多
基金the National Natural Science Foundation of China under grant 62172059 and 62072055Hunan Provincial Natural Science Foundations of China under Grant 2020JJ4626+2 种基金Scientific Research Fund of Hunan Provincial Education Department of China under Grant 19B004“Double First-class”International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant 2018IC25the Young Teacher Growth Plan Project of Changsha University of Science and Technology under Grant 2019QJCZ076.
文摘Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition,the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.
基金The National Natural Science Foundation of China under contract No.41706066the National Key R&D Program of China under contract No.2016YFC1400200the China-ASEAN Maritime Cooperation Fund
文摘High-resolution approaches such as multiple signal classification and estimation of signal parameters via rotational invariance techniques（ESPRIT） are currently employed widely in multibeam echo-sounder（MBES）systems for sea floor bathymetry,where a uniform line array is also required.However,due to the requirements in terms of the system coverage/resolution and installation space constraints,an MBES system usually employs a receiving array with a special shape,which means that high-resolution algorithms cannot be applied directly.In addition,the short-term stationary echo signals make it difficult to estimate the covariance matrix required by the high-resolution approaches,which further increases the complexity when applying the high-resolution algorithms in the MBES systems.The ESPRIT with multiple-angle subarray beamforming is employed to reduce the requirements in terms of the signal-to-noise ratio,number of snapshots,and computational effort.The simulations show that the new processing method can provide better fine-structure resolution.Then a highresolution bottom detection（HRBD） algorithm is developed by combining the new processing method with virtual array transformation.The application of the HRBD algorithm to a U-shaped array is also discuss.The computer simulations and experimental data processing results verify the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(41701499)the Sichuan Science and Technology Program(2018GZ0265)the Geomatics Technology and Application Key Laboratory of Qinghai Province(QHDX-2018-07)
文摘A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.
基金supported by the National Natural Foundation of China(Nos.41001282,40871205,and 62271408)partly by Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2021-044)。
文摘Multichannel high-resolution and wide-swath(HRWS)imaging is an advanced digital beamforming technique for future synthetic aperture radar(SAR)systems.However,radio frequency interference(RFI)is a critical concern for HRWS SAR missions,which distorts measure-ments and produces image artifacts.In this paper,the spatial cross-correlation coefficients of multichannel HRWS SAR signals are investigated for RFI detection.It is found when the two channels are correlated,RFI-polluted areas present lower coherence values than non-polluted areas in the same scenarios,which makes previous methods fail.Further,this paper studies the case of two fully decorrelated channels to maximize the coherence difference among RFI and target echoes,and RFI detection is realized by exploiting the anomaly value of coherence.Experimental results of real air-borne multichannel SAR data demonstrate that the RFI can be detected successfully.
基金Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions(No.140119001)Science&Technology Department of Liaoning Province(No.20180550831)。
文摘The value of the high-resolution data lies in the high-precision information discovery.The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevation models(DEMs).However,the results of landform element extraction generated by classical methods might be ungrounded on high-resolution DEMs.This paper presents our research on using the aspect to reinforce the applicability and robustness of the classical approaches in landform element extraction.First,according to the research of pattern recognition,we assume that aspect-enhanced landform representation is robust to rotation,scaling and affine variance.To testify the role of aspect,we respectively integrated the aspect into three classical approaches:mean curvaturebased fuzzy classification,elevation-based feature descriptor,and object-based segmentation.In the experiment,based on four types of high-resolution DEMs(1 m,2 m,4 m and 8 m),we compare each classical approaches and their corresponding aspect-enhanced approaches based on extracting the rims of two craters having different landforms,and the ridgelines and valleylines of a region covered by few vegetables and man-made buildings.In comparison to the results generated by curvature-based fuzzy classification,the aspect enhanced curvature-based fuzzy classification can effectively filter a number of noises outperforms the curvature-based one.Otherwise,the aspect-enhanced feature descriptor can detect more landform elements than the elevation-based feature descriptor.Moreover,the aspect-based segmentation can detect the main structure of landform,while the boundaries segmented by classical approaches are messing and meaningless.The systematic experiments on meter-level resolution DEMs proved that the aspect in topography could effectively to improve the classical method-system,including fuzzy-based classification,feature descriptors-based detection and object-based segmentation.The value of aspect is significantly great to be worthy of attentions in landform representation.
基金supported by the Shenzhen Science and Technology Innovation Project(No.ZDSYS20210623091808026)supported in part by the National Natural Science Foundation of China(General Program,No.42071351)+1 种基金the National Key Research and Development Program of China(No.2020YFA0608501)the Chongqing Science and Technology Bureau technology innovation and application development special(cstc2021jscx-gksb0116).
文摘Forest is the largest carbon reservoir and carbon absorber on earth.Thus,mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal.Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images.However,deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources.Therefore,there was an urgent need for a fast and accurate deforestation detection model.In this study,we proposed an interesting but effective re-parameterization deforestation detection model,named RepDDNet.Unlike other existing models designed for deforestation detection,the main feature of RepDDNet was its decoupling feature,which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage,thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged.A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images(the total area of it was over 20,000 square kilometers),and the result indicated that the model computation efficiency could be improved by nearly 30%compared with the model without re-parameterization.Additionally,compared with other lightweight models,RepDDNet also displayed a trade-off between accuracy and computation efficiency.
基金National Natural Science Foundation of China(NSFC)(61871214,61527820)Natural Science Foundation of Jiangsu Province(BK20180066)+1 种基金The Jiangsu Provincial Program for High-level Talents in Six Areas(DZXX-005)Fundamental Research Funds for the Central Universities(NS2018028,NC2018005)
文摘Photonics-based radar with a photonic de-chirp receiver has the advantages of broadband operation and real-time signal processing, but it suffers from interference from image frequencies and other undesired frequency-mixing components, due to single-channel real-valued photonic frequency mixing. In this paper, we propose a photonicsbased radar with a photonic frequency-doubling transmitter and a balanced in-phase and quadrature(I/Q)de-chirp receiver. This radar transmits broadband linearly frequency-modulated signals generated by photonic frequency doubling and performs I/Q de-chirping of the radar echoes based on a balanced photonic I/Q frequency mixer, which is realized by applying a 90° optical hybrid followed by balanced photodetectors. The proposed radar has a high range resolution because of the large operation bandwidth and achieves interference-free detection by suppressing the image frequencies and other undesired frequency-mixing components. In the experiment, a photonics-based K-band radar with a bandwidth of 8 GHz is demonstrated. The balanced I/Q de-chirping receiver achieves an image-rejection ratio of over 30 dB and successfully eliminates the interference due to the baseband envelope and the frequency mixing between radar echoes of different targets. In addition, the desired dechirped signal power is also enhanced with balanced detection. Based on the established photonics-based radar,inverse synthetic aperture radar imaging is also implemented, through which the advantages of the proposed radar are verified.
基金supported by the National Natural Science Foundation of China(Grant Nos.41771360&41842035)the National Program for Support of Top-notch Young Professionals+2 种基金the Hubei Provincial Natural Science Foundation of China(Grant No.2017CFA029)the National Key Research and Development Program of China(Grant No.2016YFB0501403)the Shenzhen Science and Technology Program(Grant No.JCYJ20180306170645080)。
文摘The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution(HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.
文摘In the last years,endoscopic techniques gained a crucial role in the treatment of colorectal flat lesions.At the same time,the importance of a reliable assessment of such lesions to predict the malignancy and the depth of invasion of the colonic wall emerged.The current unsolved dilemma about the endoscopic excision techniques concerns the necessity of a reliable submucosal invasive cancer assessment system that can stratify the risk of the post-procedural need for surgery.Accordingly,this narrative literature review aims to compare the available diagnostic strategies in predicting malignancy and to give a guide about the best techniques to employ.We performed a literature search using electronic databases(MEDLINE/PubMed,EMBASE,and Cochrane Library).We collected all articles about endoscopic mucosal resection(EMR)and endoscopic submucosal dissection(ESD)registering the outcomes.Moreover,we analyzed all meta-analyses comparing EMR vs ESD outcomes for colorectal sessile or nonpolypoid lesions of any size,preoperatively estimated as non-invasive.Seven meta-analysis studies,mainly Eastern,were included in the analysis comparing 124 studies and overall 22954 patients who underwent EMR and ESD procedures.Of these,eighty-two were retrospective,twenty-four perspective,nine casecontrol,and six cohorts,while three were randomized clinical trials.A total of 18118 EMR and 10379 ESD were completed for a whole of 28497 colorectal sessile or non-polypoid lesions>5-10 mm in size.In conclusion,it is crucial to enhance the preoperative diagnostic workup,especially in deciding the most suitable endoscopic method for radical resection of flat colorectal lesions at risk of underlying malignancy.Additionally,the ESD necessitates further improvement because of the excessively time-consuming as well as the intraprocedural technical hindrances and related complications.We found a higher rate of en bloc resections and R0 for ESD than EMR for non-pedunculated colorectal lesions.Nevertheless,despite the lower local recurrence rates,ESD had greater perforation rates and needed lengthier procedural times.The prevailing risk for additional surgery in ESD rather than EMR for complications or oncologic reasons is still uncertain.
基金Supported by the National Key Technology R & D Program of China (No.2008BAK49B04)the Project of State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University
文摘In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to extract building areas in ur- ban areas from very high resolution SAR images is presented. The results showed that this method has the ability to differentiate buildings from the complicated features in urban areas, which can be employed for land mapping and provides support for relief operations.
