Identification of grassland plants using hyperspectral remote sensing based on convolutional neural network and support vector machine

Selection of the phenological period and identification model directly affects the accuracy of plant identification. In this study, the dominant plants Seriphidium transiliense and Ceratocarpus arenarius in sagebrush desert grassland and bare land were used as identification objects. We collected grassland community hyperspectral data in April, June, and September using an SOC 710 VP hyperspectral imager to analyze the differences in ground object spectral reflectance. The optimum index factor was used to screen feature bands, and an identification model was established using convolutional neural network (CNN) and support vector machine (SVM). The results showed that: 1) In the visible light band, the spectral reflectance of the two species showed a “low-high-low” trend, and the peaks and valleys became less obvious as the month progressed. In the Red Edge band, species 2 increased rapidly. The near-infrared platform area in April showed most obvious difference in reflectance between the identified objects. 2) The bands selected using the optimum index factors were at 638.64, 789.49, and 923.79 nm. 3) The order of identification accuracy was as follows: SVM > CNN, April > September > June, bare land > S. transiliense > C. arenarius. Overall, SVM was the most accurate method for identifying the dominant plants in sagebrush desert grassland in April, with an accuracy of 92.12%.