基于高空间分辨率卫星影像的新疆阿拉尔市棉花与枣树分类

【目的】 枣树和棉花是新疆地区的两大优势作物。利用高空间分辨率遥感影像对作物进行识别,更加快速、准确地获取枣树和棉花的种植面积及其分布区域,以利于相关部门政策的制定及农作物的精确管理。【方法】 本文以新疆阿拉尔市主要农作物为研究对象,运用基于像素与面向对象的遥感影像分类方法,通过比较光谱角制图（SAM）、支持向量机（SVM）、CART决策树（DTs）、随机森林（RF）这4种机器学习算法在高空间分辨率卫星影像分类中的作物识别精度,探究影像获取时期（2016-05-10、2016-09-07、2016-10-08）及面向对象的信息提取技术对作物分类精度的影响。【结果】 5月份影像（即棉花覆膜期影像）作物分类精度最高,10月份影像次之,9月份影像最差;与基于像素的作物分类方法相比,面向对象的作物分类方法可以使各时期的作物分类总体精度得到一定提高（除SAM之外）,各时期分类精度分别提高了4.83%、7.77%、7.22%,最高分类精度分别为93.52%（2016-05-10）、85.36%（2016-09-07）、88.88%（2016-10-08）,均实现了较好的作物分类效果。【结论】 5月份（棉花覆膜期）影像对棉花和枣树分类效果最好,该时期的棉花被地膜覆盖,且枣树表现出明显的植被光谱特性,两种作物生长早期呈现出差异化的光谱特征,因此棉花和枣树的遥感识别应在作物生长早期进行;面向对象的分类方法可以综合运用光谱、纹理及空间信息,特别是纹理信息的加入,可以取得比基于像素方法更高的分类精度,且提供一种高效提取田块边界的手段,对当地农田信息化管理具有重要应用价值。在棉花和枣树识别过程中,纹理特征的重要性高于光谱和空间特征,红光和绿光波段在所有波段中对棉花和枣树的识别贡献最大。

Pixel-Based and Object-Oriented Classification of Jujube and Cotton Based on High Resolution Satellite Imagery over Alear,Xinjiang

【Objective】Jujube and cotton are widely cultivated in Xinjiang. In this study, we had an insight into the planting area and distribution area of jujube and cotton quickly and accurately by crop identification based on remote sensing technology, which was helpful to policies making and crops precise management.【Method】This paper evaluated the pixel-based and object-oriented classification methods for crop mapping using several mono-temporal (date of image acquisition: May 10, 2016; September 7, 2016; and October 8, 2016) high spatial resolution images of Alear city, Xinjiang. This research involved four different machine learning algorithms, including Spectral Angle Mapping (SAM), Support Vector Machine (SVM), CART Decision Trees (DTs) and Random Forest (RF). 【Result】 The results showed that it had the highest crop classification accuracy when using the satellite images acquired on May, followed by the satellite images acquired on October. Crop classification accuracy was the lowest when using the satellite image acquired on September. In addition, compared to pixel-based classification methods, the classification accuracies were improved when using object-oriented classification methods. The classification accuracies of each period were improved by 4.83%, 7.77%, and 7.22%, respectively. The highest classification accuracy was 93.52% (May 10, 2016), 85.36% (September 7, 2016), and 88.88% (October 8, 2016), respectively.【Conclusion】 The research results suggested that using the image acquired in May, which covers cotton seedling stage, could improve crop classification accuracy for our study area. The cotton in this period was covered by plastic film, and the jujube trees showed obvious spectral characteristics of vegetation. The two crops showed different spectral characteristics in the early stage of growth. Therefore, crop classification should be executed at the early growth stage. What’s more, spectral, texture and spatial features can be combined when using object-oriented classification methods, especially the addition of texture information, so that the overall accuracy of crop classification in each period was improved (except SAM). We can delineate the field boundaries efficiently by this method, which is important for the improvement of local crop field management. Additionally, texture features were more important than spectral and spatial features. Green and red bands had a greater contribute on crop classification.