ABSTRACT

The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance and low inter-class separability in the fine spatial resolution (FSR) remotely sensed imagery. This makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging task. To mine effectively the rich spectral and spatial information in FSR imagery, this paper proposed a Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) that classifies images at the object level by taking segmented objects (crop parcels) as basic units of analysis, thus, ensuring that the boundaries between crop parcels are delineated precisely. These segmented objects were subsequently classified using a CNN model integrated with an automatically generated scale sequence of input patch sizes. This scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger scales. The effectiveness of the SS-OCNN was investigated using two heterogeneous agricultural areas with FSR SAR and optical imagery, respectively. Experimental results revealed that the SS-OCNN consistently achieved the most accurate classification results. The SS-OCNN, thus, provides a new paradigm for crop classification over heterogeneous areas using FSR imagery, and has a wide application prospect.

摘要

农业生态系统在空间和时间上的高度动态性通常会导致精细空间分辨率 (FSR) 遥感图像中的高类内方差和低类间可分离性。这使得传统分类器基本上依赖光谱信息从 FSR 图像进行作物映射，这是一项极具挑战性的任务。为了有效挖掘 FSR 图像中丰富的光谱和空间信息，本文提出了一种基于尺度序列对象的卷积神经网络 (SS-OCNN)，该网络以分割对象（作物包裹）为基本分析单元，在对象级别对图像进行分类，从而确保精确划定作物地块之间的边界。随后使用与自动生成的输入补丁大小的比例序列集成的 CNN 模型对这些分割的对象进行分类。这种尺度序列可以通过将小尺度提取的信息逐步转换到更大尺度来有效融合不同尺度上学习的特征。SS-OCNN 的有效性分别使用具有 FSR SAR 和光学图像的两个异构农业区进行了研究。实验结果表明，SS-OCNN 始终如一地实现了最准确的分类结果。因此，SS-OCNN 为使用 FSR 图像的异质区域作物分类提供了新的范式，具有广泛的应用前景。SS-OCNN 的有效性分别使用具有 FSR SAR 和光学图像的两个异构农业区进行了研究。实验结果表明，SS-OCNN 始终如一地实现了最准确的分类结果。因此，SS-OCNN 为使用 FSR 图像的异质区域作物分类提供了新的范式，具有广泛的应用前景。SS-OCNN 的有效性分别使用具有 FSR SAR 和光学图像的两个异构农业区进行了研究。实验结果表明，SS-OCNN 始终如一地实现了最准确的分类结果。因此，SS-OCNN 为使用 FSR 图像的异质区域作物分类提供了新的范式，具有广泛的应用前景。