遥感技术已成为草业科学研究的重要手段,在植被覆盖度估算中的应用不断加强.由于遥感成像传感器的局限性和同时获取高空间与光谱分辨率图像的成本较高,导致植被观测领域的高精度估测难以从单一的遥感数据中获得.因此,需要综合源图像的关键信息,对不同分辨率的遥感图像数据进行融合,使融合后的图像具备更高的清晰度、更丰富的纹理和更详尽的光谱信息,从而提高植被覆盖度提取的精度.本研究以高分一号采集的全色、多光谱遥感图像为研究对象,对其进行了辐射定标、大气校正、正射校正、图像配准和裁剪一系列预处理,并使用主成分分析法(PCA)、亮度-色度-饱和度变换法(IHS)等7种基于分量替换的方法、Wavelet等5种基于多分辨率分析的方法、Pansharpening by convolutional neural networks(PNN)和PanNet两种基于深度学习的方法进行全色与多光谱遥感图像融合,并对比研究分析.针对最优融合算法PanNet进一步提出改进思路并加以验证,结果表明,改进后的PanNet算法的各项指标均优于改进前;最后将融合图像运用于植被覆盖度估算,证明了改进后PanNet遥感图像融合算法在植被覆盖度估算上的可操作性和优越性.
Research on vegetation coverage estimation based on panchromatic and multispectral remote sensing image fusion
Remote sensing technology has become an important tool in grassland science research,and its application in estimating grassland vegetation coverage has been strengthened.However,due to the limitations of remote sensing imaging sensors and the high cost of acquiring high spatial and spectral resolution images at the same time,high-precision estimation in the field of grassland observation is difficult to obtain from a single remote sensing data source.Therefore,it is necessary to integrate the key information of the source image and fuse remote sensing image data of different resolutions,so that the fused image has higher clarity,richer texture,and more detailed spectral information,thereby improving the accuracy of vegetation coverage extraction.In this study,the panchromatic and multispectral remote sensing images collected by GF-1 were used as the research objects.A series of preprocessing steps including radiometric calibration,atmospheric correction,orthorectification,image registration,and cropping were performed.Seven component substitution-based methods such as principal compontent analysis(PCA)and intensity-hue-saturation(IHS),five multi-resolution analysis-based methods such as Wavelet,and two deep learning-based methods,Pansharpening by convolutional neural networks(PNN)and PanNet,were used to fuse the panchromatic and multispectral remote sensing images,and comparative analysis was conducted.An improvement strategy was proposed and validated for the optimal fusion algorithm PanNet,and the results showed that all indicators of the improved PanNet algorithm were better than those of the original PanNet algorithm.Finally,the fused image was applied to estimate vegetation coverage,and the operational feasibility and superiority of the improved PanNet remote sensing image fusion algorithm in vegetation coverage estimation were demonstrated.
万方数据
维普
