CAJ | 학술논문

环境星影像具有较高的时间和空间分辨率,利用其时序遥感数据进行作物信息提取优势明显.该文以黑龙江垦区友谊农场作物为研究对象,利用2010年6月至9月共10景 HJ-CCD 数据进行作物种植分类信息提取.首先,通过 SPLINE 算法对云影响区域插值去噪,重构时间序列影像数据;其次,通过分析试验区主要作物的光谱和植被指数时序变化特征,构建基于决策树分层分类的主要作物遥感分类模型,成功提取了黑龙江友谊农场大豆、玉米和水稻的种植信息,分类总体精度达到96.33%.同时,将分类结果同基于时间序列植被指数影像的支持向量机和最大似然法分类结果相比较,结果表明,决策树分类效果最好,支持向量机次之,最大似然分类较差.研究表明,通过去云处理后构建的时间序列 HJ 卫星遥感影像,结合作物的光谱和典型植被指数时序变化特征,借助于决策树分类方法能够有效提高黑龙江垦区主要种植作物分类的准确性和精度.
배경성영상구유교고적시간화공간분변솔,이용기시서요감수거진행작물신식제취우세명현.해문이흑룡강은구우의농장작물위연구대상,이용2010년6월지9월공10경 HJ-CCD 수거진행작물충식분류신식제취.수선,통과 SPLINE 산법대운영향구역삽치거조,중구시간서렬영상수거;기차,통과분석시험구주요작물적광보화식피지수시서변화특정,구건기우결책수분층분류적주요작물요감분류모형,성공제취료흑룡강우의농장대두、옥미화수도적충식신식,분류총체정도체도96.33%.동시,장분류결과동기우시간서렬식피지수영상적지지향량궤화최대사연법분류결과상비교,결과표명,결책수분류효과최호,지지향량궤차지,최대사연분류교차.연구표명,통과거운처리후구건적시간서렬 HJ 위성요감영상,결합작물적광보화전형식피지수시서변화특정,차조우결책수분류방법능구유효제고흑룡강은구주요충식작물분류적준학성화정도.
Time-series satellite images can reflect the seasonal variation from vegetation on land surface, and have better performance than single-temporal image for vegetation classification. Multi-temporal satellite images such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) have been widely used for crop classification throughout the growth season, but exhibit some limitations due to lower spatial resolution. On the other hand, some satellite imagery data with medium- resolution (such Landsat TM) and high-resolution (such QuickBird) also display some weaknesses thanks to lower temporal resolution. Environment Satellites HJ-1A/B of China have a better spatial resolution of 30 m than MODIS and AVHRR, and a higher temporal resolution of 2 days. So it is noticeable to use the time-series images from HJ satellites for crop classification. @@@@In this paper, selecting the largest farm, Youyi Farm in Nongken Region, Heilongjiang Province, China as an example, ten HJ-CCD time-series images from June to September 2010 were used to classify crops in the farm. After atmospheric and geometric corrections, SPLINE algorithm was applied to remove cloud in images for reconstructing time-series images. By collecting three main crops (soybean, rice and corn) ground truth data with Global Positioning Systems (GPS) in fields, the band reflectance of Red and NIR, and vegetation indices of NDVI and EVI with temporal changes were extracted. The red band reflectance of rice between in June 2nd to July 12th and August 26th to September 1st had significant difference between rice with others crops. The EVI of corn was less than soybean from July 12th to September 1st. After analyzing the images through serial threshold division, masking treatment, assisting with background data and expert knowledge, the decision tree classified arithmetic was established. Then, support vector machine (SVM) and maximum likelihood supervised classification method were also used to identify these crops. @@@@The results indicated that HJ-1A/B satellite had a particular advantage in extracting vegetation information with its higher spatial and temporal resolutions. Cloud processing was of importance to reconstruct no cloud time series data. According to temporal changes of spectral reflectance and typical vegetation indices of different crop ground samples, all crops had similar tendency of NDVI. So NDVI was difficult to identify different crops. Both the red band reflectance and EVI had the remarkable spectral features to reflect the different crops growing and vegetation coverage information. Growing individual, isolated crops in bulk has become common for large-scale farms in Heilongjiang Nongken region. Planting information of soybean, corn and rice were successfully extracted based on the time series images by three methods. Comparing SVM and maximum likelihood supervised classification method with decision tree classified arithmetic, the results suggested that decision tree classified arithmetic could effectively achieve the accurate classification of main crops, its overall accuracy reached up to 96.33%. Different growth may have the similar variation tendency and so be confusion. While time series images can clearly show different spectral feature curve in different crop growth stage, avoiding wrong or missing category and greatly improving classification accuracy.