Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods fo...Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.展开更多
Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit know...Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images,which limits the understanding depth of the image content.The images contain more than just visual objects,some images contain textual information about the scene,and slightly more complex images contain relationships between individual visual objects.Firstly,this paper proposes a model using image description for feature enhancement.This model encodes images and their descriptions separately based on the question-guided coattention mechanism.This mechanism increases the feature representation of the model,enhancing the model’s ability for reasoning.In addition,this paper improves the bottom-up attention model by obtaining two image region features.After obtaining the two visual features and the spatial position information corresponding to each feature,concatenating the two features as the final image feature can better represent an image.Finally,the obtained spatial position information is processed to enable the model to perceive the size and relative position of each object in the image.Our best single model delivers a 74.16%overall accuracy on the VQA 2.0 dataset,our model even outperforms some multi-modal pre-training models with fewer images and a shorter time.展开更多
为探究植物群落空间特征与小气候因子和人体舒适度间的关系,文章以聊城市东昌湖湿地公园为研究对象,选择覆盖空间(CS)、封闭空间(ES)、半开敞空间(SOS)和开敞空间(OS)进行样地调查和小气候实测,借助RayMan Pro 3.1计算秋季生理等效温度(...为探究植物群落空间特征与小气候因子和人体舒适度间的关系,文章以聊城市东昌湖湿地公园为研究对象,选择覆盖空间(CS)、封闭空间(ES)、半开敞空间(SOS)和开敞空间(OS)进行样地调查和小气候实测,借助RayMan Pro 3.1计算秋季生理等效温度(PET)对植物群落空间进行热舒适评价。结果表明:1)不同类型的植物空间温湿度和风速均存在显著差异(P<0.05)。2)CS不存在热不适时段,OS的热不适时段最长。3)空气温度与PET呈线性正相关,相对湿度与PET呈线性负相关,风速与PET不相关。4)天空可视因子(SVF)对植物空间舒适度影响最大;绿化用地比例(GR)不影响舒适度;围合度通过影响风速间接影响舒适度。研究结果揭示了不同类型植物空间的舒适度状况,建议在公园绿地建设中增加CS的种植、适当降低植物空间种植密度等,旨在为营造更加多元且舒适的植物空间提供科学依据。展开更多
How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family ...How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.展开更多
基金funded by the Major Scientific and Technological Innovation Project of Shandong Province,Grant No.2022CXGC010609.
文摘Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.
基金supported in part by the National Natural Science Foundation of China under Grant U1911401.
文摘Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images,which limits the understanding depth of the image content.The images contain more than just visual objects,some images contain textual information about the scene,and slightly more complex images contain relationships between individual visual objects.Firstly,this paper proposes a model using image description for feature enhancement.This model encodes images and their descriptions separately based on the question-guided coattention mechanism.This mechanism increases the feature representation of the model,enhancing the model’s ability for reasoning.In addition,this paper improves the bottom-up attention model by obtaining two image region features.After obtaining the two visual features and the spatial position information corresponding to each feature,concatenating the two features as the final image feature can better represent an image.Finally,the obtained spatial position information is processed to enable the model to perceive the size and relative position of each object in the image.Our best single model delivers a 74.16%overall accuracy on the VQA 2.0 dataset,our model even outperforms some multi-modal pre-training models with fewer images and a shorter time.
文摘为探究植物群落空间特征与小气候因子和人体舒适度间的关系,文章以聊城市东昌湖湿地公园为研究对象,选择覆盖空间(CS)、封闭空间(ES)、半开敞空间(SOS)和开敞空间(OS)进行样地调查和小气候实测,借助RayMan Pro 3.1计算秋季生理等效温度(PET)对植物群落空间进行热舒适评价。结果表明:1)不同类型的植物空间温湿度和风速均存在显著差异(P<0.05)。2)CS不存在热不适时段,OS的热不适时段最长。3)空气温度与PET呈线性正相关,相对湿度与PET呈线性负相关,风速与PET不相关。4)天空可视因子(SVF)对植物空间舒适度影响最大;绿化用地比例(GR)不影响舒适度;围合度通过影响风速间接影响舒适度。研究结果揭示了不同类型植物空间的舒适度状况,建议在公园绿地建设中增加CS的种植、适当降低植物空间种植密度等,旨在为营造更加多元且舒适的植物空间提供科学依据。
基金supported by National High Technology Research and Development Program of China (863 Program)(No.2007AA01Z416)National Natural Science Foundation of China (No.60773056)+1 种基金Beijing New Star Project on Science and Technology (No.2007B071)Natural Science Foundation of Liaoning Province of China (No.20052184)
文摘How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.
