空间分辨率与纹理特征对多光谱遥感分类的影响

多光谱遥感分类与影像空间分辨率有着密切的关系,在适宜空间分辨率影像上进行地物分类能够获得更高的精度。随着遥感影像空间分辨率的提高,纹理特征被广泛用于遥感分类,但由于不同地类空间尺度不同,纹理对不同地物分类的影响程度也有所差异。本文基于高分一号2 m全色和8 m多光谱影像融合后的高空间分辨率多光谱数据构建反射率空间序列,选用3种分类方法对序列分类,并分别计算2 m融合数据及8 m多光谱影像的纹理特征,选择特征波段与相应多光谱波段组合用以分类研究,最后计算混淆矩阵评价分类精度。研究结果表明,通过回归分析得到多光谱分类的最佳空间分辨率为5 m,与其他研究中利用全色波段分类的结论一致,这说明最佳空间分辨率的选择不受光谱信息影响;对多光谱分类精度随空间分辨率变化的变化趋势分析发现,分类精度在20~30 m分辨率范围区间内快速降低,这为多光谱遥感分类数据空间分辨率的选择提供了重要参考;此外,对光谱与纹理特征结合后不同地类分类精度的变化分析显示,加入纹理特征后,冬小麦、人工建筑、有林地和水体的分类精度在2 m分辨率下分别提高了1.49%、1.51%、4.94%、1.54%,8 m分辨率下分别提高了2.95%、10.95%、5.91%、5.14%,说明引入纹理特征有利于提高分类精度,但其对不同地物类型、不同分辨率影像的影响程度不同。

Effects of Spatial Resolution and Texture Features on Multi-spectral Remote Sensing Classification

Multi-spectral remote sensing classification is strongly affected by spatial resolution while the classification accuracy is not necessarily improved by the increase of the spatial resolution. There exists an optimal resolution for each geographical entity, corresponding to its intrinsic spatial and spectral characteristics. Despite of many existing efforts, it is still far from clear how spatial resolutions affect classification accuracy. In recent studies, texture feature has been widely used as an effective factor to increase the classification accuracy of multi-spectral remote sensing. As an important characteristic of spatial information, texture feature is closely linked with morphology and distribution of objects. It may greatly increase classification accuracy in some cases that the same object has different spectra or different objects have the same spectrum. However, large uncertainties still exist in the effects of texture feature on classification for various objects at different spatial scales. This paper presents a case study implemented in Jining, Shandong Province to examine the impacts of spatial resolution and texture features on Multi-spectral Remote Sensing Images Classification using the Chinese Gaofen-1 (GF-1) satellite data. The GF-1 satellite was successfully launched on April 26, 2013, which was equipped with two types of sensors. One is the wide field view sensor (WFV sensor); the other is the panchromatic and multispectral sensor (PMS sensor) which can acquire panchromatic images at 2 m spatial resolution and multispectral images at 8 m spatial resolution. First, we carried out radiometric calibration, atmospheric correction and precise geometric rectification for original images. Then, we conducted data fusion based on Gram-Schmidt transformation and performed the expansion of spatial scales for the establishment of the spatial series of the reflectance (2~10 m at an interval of 1 m, 10~90 m at an interval of 10 m). Second, we generated the classification results using three popular approaches, i.e., the Maximum Likelihood Classification (MLC), the Support Vector Machine (SVM) and the Artificial Neural Network (ANN). Third, after the calculation of texture features of 2 m and 8 m for reflectance images, separately, the Principal Components Analysis (PCA) method was used for texture features selection. The data combining key features with corresponding multi-spectral bands were classified based on the ANN approach. Finally, we evaluated the classification accuracies using the confusion matrix. Our final regression analysis suggested an optimal spatial resolution of 5 m for the multi-spectral classification, implying that the optimal selection of the spatial resolution is not affected by the spectral information of multi-spectral remote sensing images. Further analysis of changing trend of accuracies along with spatial resolution showed a sharp decrease when the spatial resolution is coarser than 20-30 m. The results of the impacts of texture feature suggested that, compared with the classification by spectral information, the accuracies of winter wheat, architecture, forest and water were increased by 1.49%, 1.51%, 4.94% and 1.54% at 2 m resolution, and 2.95%, 10.95%, 5.91% and 5.14% at 8 m resolution, respectively, when the texture features were introduced. We concluded that, compared with the classification accuracy of the spectral information, considering texture feature effects may improve the classification accuracies, to varying degrees, at different spatial resolutions, especially when an appropriate resolution was chosen. Our findings are practically helpful for the optimal selection of spatial resolution for multi-spectral remote sensing classification. In the next step, we will further examine the impacts of spatial resolution and texture features at larger scales. In addition, the impacts of texture features at different phenological stages will also be investigated.