超分辨率重构遥感图像分类研究

Classification Research of Remote Sensing Image Based on Super Resolution Reconstruction

通常情况下,超分辨率技术能够使图像获得更好的视觉质量,从而有利于图像的目视解译。然而,超分辨率技术能否提升图像在应用于分类、识别等其他更高级计算机视觉任务时的效果呢?结合计算机视觉技术的发展现状,首先训练一个具有良好性能的图像分类模型,用于对经过不同超分辨率方法处理后的图像进行分类。利用分类准确率来衡量超分辨率重构处理对图像分类任务的影响。在遥感分类数据集上的实验显示,相比插值方法,经过超分辨率处理的图像在分类模型中取得了更高的分类准确率。这一结果说明,经过良好设计的超分辨率模型不仅能使低质图像获得更好的视觉质量,在分类模型中也能够被更准确地归入所属类别。证实了经过良好设计的超分辨率模型对于图像分类任务具有促进作用,并且还能有效提升低质图像在计算机视觉中的感知效果。

Usually,super-resolution technology makes the image get better visual quality,which is conducive to the visual interpretation of the image.However,will super-resolution technology improve the effect of image in the application of classification,recognition and other more advanced computer vision tasks?Combined with the development of computer vision technology,a new image classification model is trained to classify the images processed by different super-resolution methods.By measuring the classification accuracy of different types of images,the promotion effect of different super-resolution methods on image classification task is measured.Experimental results on remote sensing classification data set show that the well-designed super-resolution model has obvious effect on enhancing low-quality images.Compared with the interpolation method,the image processed by super-resolution achieves higher classification accuracy in the classification model,which proves that the well-designed super-resolution model can promote the image classification task.It is confirmed that super-resolution can effectively improve the perception effect of image in computer vision.