A Fourier Frequency Domain Convolutional Neural Network for Remote Sensing Crop Classification Considering Global Consistency and Edge Specificity

Abstract

The complex remote sensing image acquisition conditions and the differences in crop growth create many crop classification challenges. Frequency decomposition enables the capture of the feature information in an image that is difficult to discern. Frequency domain filters can strengthen or weaken specific frequency components to enhance the interclass differences among the different crops and can reduce the intraclass variations within the same crops, thereby improving crop classification accuracy. In concurrence with the Fourier frequency domain learning strategy, we propose a convolutional neural network called the Fourier frequency domain convolutional (FFDC) net, which transforms feature maps from the spatial domain to the frequency spectral domain. In this network, the dynamic frequency filtering components in the frequency spectral domain are used to separate the feature maps into low-frequency and high-frequency components, and the strength and distribution of the different frequency components are automatically adjusted to suppress the low-frequency information variations within the same crop, enhancing the overall consistency of the crops. Simultaneously, it is also used to strengthen the high-frequency information differences among the different crops to widen the interclass differences and to achieve high-precision remote sensing crop classification. In the test areas, which are randomly selected in multiple farms located far from the sampling area, we compare our method with other methods. The results demonstrate that the frequency-domain learning approach better mitigates issues, such as incomplete crop extractions and fragmented boundaries, which leads to higher classification accuracy and robustness. This paper applies frequency-domain deep learning to remote sensing crop classification, highlighting a novel and effective solution that supports agricultural management decisions and planning.