Classification of time-series of vegetation indices (VIs) can be a reliable strategy for identifying and monitoring different crop types. Recently, with the advent of new sensors, the time-series data with high spatial and temporal resolutions have become widely available and used for constructing various VIs time-series. These high-resolution time-series, in addition to temporal information about the crops’ phenology, contain valuable information about the spatial patterns of croplands. This information can be used to increase the performance of crop classification. In order to properly extract both spatial and temporal information from the time-series of VIs, we proposed the concept of histogram-based spatio-temporal (HST) features. These features represent each pixel in a time-series by the histogram of its spatio-temporal neighborhood. The HST features, like any other histogram-based features, are characterized by high dimensionality and sparseness. Consequently, the common classification algorithms cannot be employed for their classification. To address this issue, we presented Support Vector Machines (SVM) using an intersection kernel, which is specifically proposed for classification of histogram-based features. Time-series of three different vegetation indices, namely, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Red Edge Normalized Difference Vegetation Index (NDVI_RE) were considered to evaluate the performance of the HST features. The results of experimental tests showed that the HST features by yielding the overall accuracy of 88.31%, 87.27% and 84.36% for NDVI_RE, NDVI, and SAVI respectively are much more informative than other textural features used for comparison. Moreover, we provided a detailed analysis of the performance of the HST features concerning the size of the spatio-temporal neighborhood and the number of histogram’s bins.

植被指数时间序列的分类可以是识别和监测不同作物类型的可靠策略。近来，随着新传感器的出现，具有高空间和时间分辨率的时间序列数据已变得广​​泛可用，并用于构建各种VI的时间序列。除了有关作物物候的时间信息之外，这些高分辨率时间序列还包含有关农田空间格局的宝贵信息。该信息可用于提高农作物分类的性能。为了从VI的时间序列中正确提取空间和时间信息，我们提出了基于直方图的时空（HST）特征的概念。这些特征通过其时空邻域的直方图表示时间序列中的每个像素。像任何其他基于直方图的特征一样，HST特征的特征是高维度和稀疏性。因此，不能将通用分类算法用于其分类。为了解决这个问题，我们提出了一种使用相交核的支持向量机（SVM），该核是专门为基于直方图的特征进行分类而提出的。三种不同植被指数的时间序列，即归一化植被指数（NDVI），土壤调整植被指数（SAVI）和红边归一化植被指数（NDVI）我们提出了一种使用相交核的支持向量机（SVM），该核是专门为基于直方图的特征进行分类而提出的。三种不同植被指数的时间序列，即归一化植被指数（NDVI），土壤调整植被指数（SAVI）和红边归一化植被指数（NDVI）我们提出了一种使用相交核的支持向量机（SVM），该核是专门为基于直方图的特征进行分类而提出的。三种不同植被指数的时间序列，即归一化植被指数（NDVI），土壤调整植被指数（SAVI）和红边归一化植被指数（NDVI）_RE）被认为可以评估HST功能的性能。实验测试结果表明，HST功能通过分别为NDVI _RE，NDVI和SAVI产生88.31％，87.27％和84.36％的总体准确度，比其他用于比较的纹理特征提供了更多的信息。此外，我们提供了有关时空邻域大小和直方图箱数的HST功能性能的详细分析。