A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint (MPMAP) combined with evaluating image detail parameter D, has b...A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint (MPMAP) combined with evaluating image detail parameter D, has been proposed. The advantage of super-resolution algorithm MPMAP incorporated with parameter D lies in the fact that super-resolution algorithm MPMAP model is discrete, which is in accordance with remote-sensing imaging model, and the algorithm MPMAP is proved applicable to linear and non-linear imaging models with a unique solution when noise is not severe. According to simulation experiments for practical images, super-resolution algorithm MPMAP can retain image details better than most of traditional restoration methods; at the same time, the proposed parameter D can help to identify real point spread function (PSF) value of degradation process. Processing result of practical remote-sensing images by MPMAP combined with parameter D are given, it illustrates that MPMAP restoration scheme combined PSF estimation has a better restoration result than that of Photoshop processing, based on the same original images. It is proved that the proposed scheme is helpful to offset the lack of resolution of the original remote-sensing images and has its extensive application foreground.展开更多
针对地震后利用遥感图像检测受损建筑物,本研究提出了一种基于改进YOLOv3模型的受损建筑物识别方法。首先,通过深入分析尺度特征,对主干网络进行了针对性优化,增强了模型对微小目标特征的捕获能力。其次,引入感受野模块(Receptive Field...针对地震后利用遥感图像检测受损建筑物,本研究提出了一种基于改进YOLOv3模型的受损建筑物识别方法。首先,通过深入分析尺度特征,对主干网络进行了针对性优化,增强了模型对微小目标特征的捕获能力。其次,引入感受野模块(Receptive Field Block,RFB),拓宽了特征图的感知域,提高了对小尺寸目标的检测灵敏度。最后,对锚框及其分配策略进行了精细调整。实验结果表明,相较于原始YOLOv3模型,所提方法检测精度和检测速度均大幅提升,并且在抗噪能力上展现出显著优势;与已有识别方法相比,平均检测精度分别提升了4.8%和5.4%;在处理复杂的目标检测任务时展现出更优的性能和更强的鲁棒性,有效实现了高分辨率遥感图像中受损建筑物的准确识别。展开更多
现有大多遥感图像超分辨率方法,无法充分挖掘图像中混合尺度的自相似性信息和跨尺度区域间的关联信息,且忽略了频率域对感知图像高频信息的能力。针对这一问题,本文提出了一种空间自适应及频率融合网络(Spatial Adaptation and Frequenc...现有大多遥感图像超分辨率方法,无法充分挖掘图像中混合尺度的自相似性信息和跨尺度区域间的关联信息,且忽略了频率域对感知图像高频信息的能力。针对这一问题,本文提出了一种空间自适应及频率融合网络(Spatial Adaptation and Frequency Fusion Network,SAF2Net)。SAF2Net首先引入一种混合尺度空间自适应特征调制模块,采用类似于特征金字塔的方式获取不同尺度下的判别特征,丰富多尺度特征的表达能力。随后,设计了一个全局多尺度感受野选择块,挖掘跨尺度区域间的关联特征。在此基础上,引入空间自适应选择块和频率分离选择块,融合空间-频率互补信息以增强局部特征,提高模型对图像高频内容的建模能力。在两个公开遥感图像数据集上进行多组实验,SAF2Net获得的定量评价指标结果均优于其他对比方法。以UCMerced数据集3倍超分辨率为例,本文方法相较于次优方法HAUNet,PSNR和SSIM分别提升了0.11 dB和0.0033;在主观视觉质量方面,SAF2Net能够恢复出更多清晰的纹理细节。实验结果表明,本文所提出的SAF2Net能够从两个不同的角度挖掘混合尺度全局信息,并有效融合空间-频率互补特征,在遥感图像超分辨率任务中表现出具有竞争力的重建性能。展开更多
文摘A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint (MPMAP) combined with evaluating image detail parameter D, has been proposed. The advantage of super-resolution algorithm MPMAP incorporated with parameter D lies in the fact that super-resolution algorithm MPMAP model is discrete, which is in accordance with remote-sensing imaging model, and the algorithm MPMAP is proved applicable to linear and non-linear imaging models with a unique solution when noise is not severe. According to simulation experiments for practical images, super-resolution algorithm MPMAP can retain image details better than most of traditional restoration methods; at the same time, the proposed parameter D can help to identify real point spread function (PSF) value of degradation process. Processing result of practical remote-sensing images by MPMAP combined with parameter D are given, it illustrates that MPMAP restoration scheme combined PSF estimation has a better restoration result than that of Photoshop processing, based on the same original images. It is proved that the proposed scheme is helpful to offset the lack of resolution of the original remote-sensing images and has its extensive application foreground.
文摘针对地震后利用遥感图像检测受损建筑物,本研究提出了一种基于改进YOLOv3模型的受损建筑物识别方法。首先,通过深入分析尺度特征,对主干网络进行了针对性优化,增强了模型对微小目标特征的捕获能力。其次,引入感受野模块(Receptive Field Block,RFB),拓宽了特征图的感知域,提高了对小尺寸目标的检测灵敏度。最后,对锚框及其分配策略进行了精细调整。实验结果表明,相较于原始YOLOv3模型,所提方法检测精度和检测速度均大幅提升,并且在抗噪能力上展现出显著优势;与已有识别方法相比,平均检测精度分别提升了4.8%和5.4%;在处理复杂的目标检测任务时展现出更优的性能和更强的鲁棒性,有效实现了高分辨率遥感图像中受损建筑物的准确识别。
文摘现有大多遥感图像超分辨率方法,无法充分挖掘图像中混合尺度的自相似性信息和跨尺度区域间的关联信息,且忽略了频率域对感知图像高频信息的能力。针对这一问题,本文提出了一种空间自适应及频率融合网络(Spatial Adaptation and Frequency Fusion Network,SAF2Net)。SAF2Net首先引入一种混合尺度空间自适应特征调制模块,采用类似于特征金字塔的方式获取不同尺度下的判别特征,丰富多尺度特征的表达能力。随后,设计了一个全局多尺度感受野选择块,挖掘跨尺度区域间的关联特征。在此基础上,引入空间自适应选择块和频率分离选择块,融合空间-频率互补信息以增强局部特征,提高模型对图像高频内容的建模能力。在两个公开遥感图像数据集上进行多组实验,SAF2Net获得的定量评价指标结果均优于其他对比方法。以UCMerced数据集3倍超分辨率为例,本文方法相较于次优方法HAUNet,PSNR和SSIM分别提升了0.11 dB和0.0033;在主观视觉质量方面,SAF2Net能够恢复出更多清晰的纹理细节。实验结果表明,本文所提出的SAF2Net能够从两个不同的角度挖掘混合尺度全局信息,并有效融合空间-频率互补特征,在遥感图像超分辨率任务中表现出具有竞争力的重建性能。
