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
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。
