How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征...[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征的尺度分割思想与基于物候学的DESTIN(delineation by fusing spatial and temporal information)分割算法,提出了基于多尺度及DESTIN约束的高分遥感影像农田田块语义分割方法。[结果]多尺度与DESTIN约束下基于深度模型的田块语义分割有效改善模型出现的区域不封闭、边缘不贴合、噪点和块状模糊等问题,一定程度修正了深度模型语义分割的错误识别,IoU指标在2个测试集上分别达到94.08%和90.79%,相较深度模型的遥感影像田块语义分割分别提高1.65%和2.32%,对研究区域的田块提取区域更完整、精度更高。[结论]多尺度及DESTIN约束进一步改善了田块语义分割问题,有助于提高高分遥感影像的田块识别精度。展开更多
In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have differ...In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.展开更多
针对遥感地物建筑物图像目标尺度差异大、样本空间分布不均衡、地物边界模糊、场景区域跨度大所导致的分割效果不佳问题,本文提出一种融合动态特征增强高精度遥感建筑物分割算法。首先,构建New_GhostNetV2网络,利用自适应上下文感知卷积...针对遥感地物建筑物图像目标尺度差异大、样本空间分布不均衡、地物边界模糊、场景区域跨度大所导致的分割效果不佳问题,本文提出一种融合动态特征增强高精度遥感建筑物分割算法。首先,构建New_GhostNetV2网络,利用自适应上下文感知卷积,增强算法对样本空间特征的捕捉能力。其次,采用Ghost Convolution结合跳跃连接和特征分支策略设计多层级信息增强模块,增强特征整合。随后引入级联注意力CGA(cascaded group attention),通过组内独立注意力计算,加强模型对多样化地物形态的适应性。最后,通过动态深度特征增强器构造特征融合模块,进一步加强模型捕获能力。在WHU数据集上实验结果表明:改进算法较基线模型F1-Score提高8.57%,mIoU提高12.48%,与其他主流语义分割模型相比,改进DeepLabv3+具有更好的分割精度。展开更多
遥感图像目标检测在军事侦察、智慧农业等领域意义重大,特别是小目标检测一直获得持续关注。然而,遥感图像中的小目标面临特征信息不足、检测难度大等问题,成为困扰遥感检测应用发展的最大障碍。为此,提出YOLO-HF(you only look once-hy...遥感图像目标检测在军事侦察、智慧农业等领域意义重大,特别是小目标检测一直获得持续关注。然而,遥感图像中的小目标面临特征信息不足、检测难度大等问题,成为困扰遥感检测应用发展的最大障碍。为此,提出YOLO-HF(you only look once-hybrid feature)算法,该算法在传统YOLOv7模型的网络中,引入通道注意力和自注意力的混合注意力机制提取目标深层特征,并将浅层特征和深层特征进行融合,增加局部特征的丰富性;为进一步加强对全局信息的关注,在提取特征后为小尺度目标添加全局注意力机制,实现全局特征表达能力的提升;为避免传统损失函数对小目标位置偏差敏感,导致检测效果不佳,选择使用一种新的度量方式,将其嵌入边界框损失函数的计算中,从而加快损失函数的收敛,实现小目标检测精度的提升。实验结果表明:与传统YOLOv7算法相比,所提算法在RSOD和NWPU VHR-10数据集上均表现出优越性,特别地,在RSOD数据集上均值平均精度提升了2.90%,在NWPU VHR-10数据集上均值平均精度实现了3.61%的提升。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
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
文摘[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征的尺度分割思想与基于物候学的DESTIN(delineation by fusing spatial and temporal information)分割算法,提出了基于多尺度及DESTIN约束的高分遥感影像农田田块语义分割方法。[结果]多尺度与DESTIN约束下基于深度模型的田块语义分割有效改善模型出现的区域不封闭、边缘不贴合、噪点和块状模糊等问题,一定程度修正了深度模型语义分割的错误识别,IoU指标在2个测试集上分别达到94.08%和90.79%,相较深度模型的遥感影像田块语义分割分别提高1.65%和2.32%,对研究区域的田块提取区域更完整、精度更高。[结论]多尺度及DESTIN约束进一步改善了田块语义分割问题,有助于提高高分遥感影像的田块识别精度。
文摘In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.
文摘针对遥感地物建筑物图像目标尺度差异大、样本空间分布不均衡、地物边界模糊、场景区域跨度大所导致的分割效果不佳问题,本文提出一种融合动态特征增强高精度遥感建筑物分割算法。首先,构建New_GhostNetV2网络,利用自适应上下文感知卷积,增强算法对样本空间特征的捕捉能力。其次,采用Ghost Convolution结合跳跃连接和特征分支策略设计多层级信息增强模块,增强特征整合。随后引入级联注意力CGA(cascaded group attention),通过组内独立注意力计算,加强模型对多样化地物形态的适应性。最后,通过动态深度特征增强器构造特征融合模块,进一步加强模型捕获能力。在WHU数据集上实验结果表明:改进算法较基线模型F1-Score提高8.57%,mIoU提高12.48%,与其他主流语义分割模型相比,改进DeepLabv3+具有更好的分割精度。
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。
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
