考虑点扩散函数效应的光谱遥感图像超分辨率制图

Considering Point Spread Function Effect for Spectral Remote Sensing Image Super-Resolution Mapping

由于硬件设备的限制和土地覆盖类别的多样性,采集得到的光谱遥感图像的空间分辨率有时较为粗糙,导致大量混合像元产生,严重影响了土地覆盖类型空间分布的制图精度。超分辨率制图技术可以有效地处理光谱遥感图像中的混合像元,获得准确的地物类别分布信息。在遥感大数据背景下,来自同一卫星采集同一区域的多位移图像可以作为辅助数据改进超分辨率制图结果。然而,目前多位移图像插值超分辨率制图方法很少有效地考虑点扩散函数效应影响,导致制图结果精度降低。为了解决这一问题,本文提出了一种考虑点扩散函数效应的多位移光谱遥感图像超分辨率制图方法,改善土地覆盖类别制图结果。在所提出的方法中,首先,对多位移图像进行光谱解混,以生成粗糙丰度图像。然后,在考虑点扩散函数效应的情况下,对粗糙丰度图像先采用面到点克里格法,然后进行理想方波滤波,得到改善的粗糙丰度图像。接下来,通过插值对改善的粗糙丰度图像进行上采样以获得上采样丰度图像,并对上采样的所有丰度图像进行整合以获得精细丰度图像。最后,根据精细丰度图像提供的类别比例信息,使用类别分配方法将类别标签分配给亚像元,以获得理想的制图结果。实验结果表明,通过减少点扩散函数效应影响,所提出的方法展示出最佳性能表现,例如在美国华盛顿特区数据集实验结果的总体精度分别达到77.63%和Kappa系数达到0.7263。

Because of hardware limitations and the diversity of land-cover classes,the spatial resolution of collected spectral remote sensing images is sometimes coarse,producing many mixed pixels,which seriously affects the accuracy when mapping the spatial distribution of land-cover classes.Super-resolution mapping technology can effectively process the mixed pixels in a spectral remote sensing image and obtain accurate information on the distribution of landcover classes.In the context of remote sensing big data,multiple sub-pixel shifted images collected by the same satellite in the same area can be used as auxiliary data to improve the super-resolution mapping results.However,the current methods used for super-resolution mapping in multiple sub-pixel shifted-image interpolation are rarely effective in considering the effect of the point spread function,which reduces the accuracy of the mapping results.To address this issue,this paper proposes a method that considers the point-spread function effect for spectral remote sensing image superresolution mapping.In the proposed method,multiple sub-pixel shifted images are first unmixed to produce coarse fractional images.Then,while considering the point-spread function effect,improved coarse fractional images are obtained by applying the area-to-point kriging and then the ideal square-wave filter to the coarse fractional images.Next,the improved coarse fractional images are upsampled by interpolation to obtain upsampled fractional images,which are then integrated to obtain fine fractional images.Finally,based on the class proportion information from the fine fractional images,the class allocation method is utilized to assign class labels to sub-pixels to obtain the ideal mapping results.Experimental results showed that the proposed method exhibited the optimal performance.For example,the overall accuracy and Kappa coefficient of the experimental results for the Washington,D.C.,USA Dataset reached 77.63%and 0.7263,respectively.