A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions

Abstract Heterogeneous crop identification has been the subject of much concern, since smallholder farms less than 1 ha are the main agricultural form in many areas, especially China. Remote sensing with high spectral and spatial resolutions via aerial platforms such as unmanned aerial vehicles (UAV) provides a potential alternative technique for the monitoring of heterogeneous crops in smallholder agriculture. Although this new type of remote sensing data with high spectral and spatial resolutions provides the possibility of fine classification, it also brings some challenges, such as bands contaminated with severe noise, the nonuniform distribution of the discriminative spectral information, and the spectral variability of crops. In this study, we attempted to resolve these problems by developing a robust spectral-spatial agricultural crop mapping method based on conditional random fields (SCRF), which learns the sensitive spectral information of the crops by a spectrally weighted kernel, and uses the spatial interaction of pixels to improve the classification performance. Data from a manned aircraft platform and a UAV platform were chosen to validate the effectiveness of the proposed algorithm. The experimental results showed that the proposed algorithm can effectively use the relative utility of each spectral band to detect the bands contaminated with severe noise, and it uses the spectrally weighted kernel to consider the sensitive spectral information of the crops. The algorithm with only a spectrally weighted kernel showed an improvement of more than 4% over the classical support vector machine and random forest methods. Moreover, the spatial information was proved to be of crucial importance for crop classification, and both the object-oriented method and the proposed SCRF method can improve the classification performance in terms of both visualization and the quantitative metrics by considering the spatial information. Compared with the object-oriented method, SCRF can deliver a better classification performance, with an accuracy improvement of more than 2%.