为提高基于国产环境与灾害监测预报卫星(HJ-1/CCD)影像大范围提取冬小麦种植面积的精度,以江苏省宿迁市沭阳县为研究区域,对冬小麦拔节期30 m×30 m的HJ-1/CCD多光谱影像和2 m×2 m的高分1号卫星全色影像(GF-1/PMS)进行融合与面向对象分类研究.将GF-1/PMS全色影像进行8、16和24 m重采样,得到4种空间分辨率(含2 m)的全色影像,分别与HJ-1/CCD多光谱影像利用光谱锐化法(Gram-Schmidt,GS)进行融合.通过对融合影像进行质量评价,选择适合研究区冬小麦种植田块格局的适宜尺度影像.将HJ-1/CCD多光谱影像重采样,得到与适宜尺度融合影像相同尺度的影像,在两景影像中分别选取包含光谱、纹理信息的训练融合影像样本(samples of fused image,SFI)和重采样影像样本(samples of resampling image,SRI),采用面向对象分类方法对适宜尺度融合影像(fused image,FI)和重采样影像(resampling image,RI)进行冬小麦种植面积提取.结果表明,16 m×16 m融合影像的效果优于2 m×2 m、8 m×8 m和24 m×24 m 融合影像,其均值、标准差、平均梯度和相关系数分别为161.15、83.01、4.55和0.97.面向对象分类后,SFI对重采样影像RI16m分类的总体精度为92.22%,Kappa系数为0.90.SFI对融合影像FI16m分类的总体精度为94.44%,Kappa系数为0.93.SRI对重采样影像RI16m分类的总体精度为84.44%,Kappa系数为0.80.SFI对融合影像FI16m分类效果最好,说明基于融合影像和融合影像提取样本(SFI)结合的面向对象分类方法能准确提取冬小麦种植面积.另外,重采样影像和融合影像提取样本(SFI)相结合的面向对象分类方法也可较好提取冬小麦种植面积.为利用国产中空间分辨率HJ-1/CCD卫星和高分1号卫星融合影像有效提取大区域冬小麦种植面积信息提供了参考.
Abstract
In order to improve the accuracy of extracting large-scale winter wheat planting area from the data of domestic environment and disaster monitoring and forecasting satellite(HJ-1/CCD).This study took Shuyang County,Suqian City,Jiangsu Province as the research area.The fusion and object-oriented classification of the 30 m×30 m HJ-1/CCD multispectral image and the 2 m×2 m GF-1 panchromatic image(GF-1/PMS)at the jointing stage of winter wheat were carried out.The GF-1/PMS panchromatic images were resampled at 8,16 and 24 m,and panchromatic images with four spatial resolutions(including 2 m)were obtained,which were fused with HJ-1/CCD multispectral images by Gram-Schmidt(GS),respectively.Through the quality evaluation of the fused image,the appropriate scale image suitable for the pattern of winter wheat planting fields in the study area was selected.The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the appropriate scale fused image.In the 2 scene images,the training samples SFI(samples of fused image)and SRI(samples of resampling image)containing spectral and texture information were selected respectively,the object-oriented classification method was used to extract the planting area of winter wheat from fused image(FI)and resampling image(RI).The results showed that the fusion effect of 16 m×16 m fused images was better than 2 m×2 m,8 m×8 m and 24 m×24 m fused images,and the mean,standard deviation,average gradient and correlation coefficient were 161.15,83.01,4.55 and 0.97.After object-oriented classification,the overall accuracy of SFI for the classification of resampled image RI16m was 92.22%,and the Kappa coefficient was 0.90.The overall accuracy of SFI for the classification of fused image FI16m was 94.44%,and the Kappa coefficient was 0.93.The overall accuracy of SRI for the classification of resampled image RI16m was 84.44%,and the Kappa coefficient was 0.80.The classification effect of SFI for the fused image FI16m was the best,indicating that the object-oriented classification method combined with the fused image and the extraction samples of fused image(SFI)could accurately extract the winter wheat planting area.In addition,the object-oriented classification method combining resampling image and the extraction samples of fused image(SFI)could also better extract the winter wheat planting area.This method provided a reference for the effective extraction of large-scale winter wheat planting area information combined with domestic medium-spatial resolution HJ-1/CCD images and GF-1 satellite images.
维普
万方数据