Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

Abstract

Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food security. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions.