摘要
目的提出一种利用高空间分辨率Landsat8 OLI全色影像和高时间分辨率MODIS影像进行融合的方法,构建出高时空分辨率遥感数据,提供一种监测农作物种植面积新思路,为农业生产信息化奠定科学的理论依据.方法以沈阳市法库县为例,基于色彩超球面锐化(HCS)方法,利用多光谱Landsat8 OLI全色影像数据与MODIS-NDVI时间序列数据相结合的手段,对其进行预处理后,根据该地区的物候数据和具有可信度的样本数据进行面积估算,并统计出玉米种植面积的制图精度和用户精度;对Landsat8 OLI全色影像数据和MODIS-NDVI时间序列数据进行融合处理,再利用马氏距离分类法对高时空分辨率遥感数据进行玉米种植面积提取.结果融合后的高时空遥感数据对玉米种植面积的识别效果较好,制图精度和用户精度分别达到89.62%、99.71%.结论 HCS方法适用于高时间数据和高空间数据的融合,融合后的影像保持了原有的光谱特征及空间细节纹理.
The method which is combination of Landsat8 OLI panchromatic image but also and MODIS image of high temporal resolution is proposed to construct remote sensing data with high temporal and spatial resolution for monitoring crops planting area,which will lay a scientific theoretical basis for the information of agricultural production.Faku County of Shenyang is taken as an example.Its area is pretreated by the combining of multi spectral Landsat8 OLI panchromatic image data and MODIS-NDVI time series data on the basis of the HCS method.According to thephenological data of the area and sample data with credibility,the size of the area is estimated and mapping precision and user accuracy of corn planting area are counted out;Based on the fusion of Landsat8 OLI panchromatic image data and MODIS-NDVI time series data,the area of corn planting is extracted from high temporal and spatial resolution remote sensing data by Mahalanobis distance classification.The recognition performance of fused high temporal and spatial remote sensing data on corn planting area is good enough,and the mapping accuracy and user accuracy reached 89.62% and 99.71% respectively.HCS method is applicable to the fusion of high temporal data and high spatial data,and the fused image keeps the spectral characteristic and the spatial texture details of the original.
作者
刘玉梅
杨文波
马运涛
LIU Yumei YANG Wenbo MA Yuntao(School of Trans portation Engineering, Shenyang Jianzhu University, Shenyang, China, 110168)
出处
《沈阳建筑大学学报(自然科学版)》
CAS
CSCD
北大核心
2017年第2期314-322,共9页
Journal of Shenyang Jianzhu University:Natural Science
基金
国家自然科学基金项目(51178277)
辽宁省教育厅一般项目(L2013232)
关键词
时空数据融合
地物分类
种植面积
HCS方法
temporal spatial data fusion
ground object classification
planting area
HCS method