农业区域多光谱遥感影像亚像元定位研究

在对亚像元定位空间引力模型改进的基础上,提出了一种基于二次引力计算的亚像元定位模型,并在不同退化尺度下开展基于空间引力模型、像元交换模型和二次引力计算模型的亚像元定位精度比较研究。其中,数据源为人工影像和国产高分一号8 m空间分辨率遥感影像,研究对象为中国北方黄淮海区典型区域夏收作物。结果表明,在不同退化尺度条件下,所提二次引力计算模型(DSGM)可有效进行亚像元定位,且定位精度均优于空间引力模型和像元交换模型。其中,在亚像元分割尺度为6的人工影像实验中,二次引力计算模型亚像元定位总体精度和kappa系数分别为93.90%和0.818,比K-mean硬分类精度分别提高3.76%和0.254,比空间引力模型亚像元定位精度分别提高2.25%和0.160,比像元交换模型亚像元定位精度分别提高2.45%和0.173;在亚像元分割尺度为4的遥感影像实验中,二次引力计算模型亚像元定位总体精度和kappa系数分别为83.13%和0.742,较K-mean硬分类精度分别提高9.50%和0.154,较空间引力模型亚像元定位精度分别提高5.44%和0.088,较像元交换模型亚像元定位精度分别提高6.39%和0.104。

Multispectral Images Sub-pixel Mapping in Agricultural Region

In order to obtain spatial features distribution from mixed pixels of remote sensing image and further increase accuracy of crop classification and recognition from remote sensing, a double-calculated spatial gravity model (DSGM) based on improvement of spatial attraction model was put forward and applied in research of multispectral images classification and identification in agriculture region at sub-pixel level. Law of gravity was used to describe the spatial correlation and calculate attraction between pixels. Based on the above research, the initialization algorithm of the pixel swapping model (PSM) was improved by spatial attraction model (SAM), and the optimization algorithm of PSM was improved respectively. Finally, all of the models of PSM, SAM and DSGM were applied to the sub-pixel mapping experiments of multispectral images in agricultural region and sub-pixel mapping accuracies of models were compared with each other. The study areas located in typical farming area of Huang-Huai-Hai Plain in North China, and artificial imagery in different degradation scales and GF-1 remote sensing imagery were used as the data sources in the experiment. The final results indicated that (DSGM) model could map effectively at sub-pixel level and its mapping accuracy was superior to those of PSM and SAM. Among them, in artificial image experiment, when sub-pixel degradation scale was 6, overall accuracy and kappa coefficient of DSGM were 93.90% and 0.818, respectively. Compared with K-mean classification, the DSGM model could improve overall accuracy and kappa coefficient by 3.76% and 0.254, respectively. Compared with SAM, DSGM could improve overall accuracy and kappa coefficient by 2.25% and 0.160, respectively. Compared with PSM, DSGM could improve overall accuracy and kappa coefficient by 2.45% and 0.173, respectively. In remote sensing image experiment, when sub-pixel degradation scale was 4, overall accuracy and kappa coefficient of DSGM were 83.13% and 0.742, respectively. Compared with the K-mean classification, DSGM could improve the overall accuracy and kappa coefficient by 9.50% and 0.154, respectively. Compared with SAM, DSGM could improve the overall accuracy and kappa coefficient by 5.44% and 0.088, respectively. Compared with PSM, DSGM could improve the overall accuracy and kappa coefficient by 6.39% and 0.104, respectively. It was seen that DSGM model had feasibility and applicability in sub-pixel mapping, and it could provide a new way to better surpass the limits of remote sensing image spatial resolution. DSGM could further improve accuracy of crop remote sensing classification and recognition and provide strong technical support to obtain accurate information for agricultural remote sensing.