Accurate Extraction of Mountain Grassland From Remote Sensing Image Using a Capsule Network

Due to an increasing demand of animal husbandry in arid (semiarid) area in China, grassland monitoring based on big data has become a proliferating research focus in recent years. However, the spectral of grass is interfered by topographic relief or that of forest in remote sensing image classification, leading to confusing pixels. In this letter, Tangbula grassland in the middle section of the Tianshan Mountains in Xinjiang (China) was selected as the research area. Upon analysis, we developed a novel composite multifeature deep learning method of capsule network to realize rapid and high-precision remote sensing recognition of the mountainous grassland, through combining the spectral bands with all the extracted features [normalized difference vegetation index (NDVI), topographic, and texture]. Using the new method, the accuracy of the grassland and overall classifications reached the highest values of 91.60% and 96.13%, respectively, greater than those of 85.58% and 92.18%, respectively, of normal classifications without input from texture and topographic features. Compared with the other methods, the method we applied is better than support vector machine (SVM), random forest, and artificial neural network in terms of grassland extraction and classification accuracy.

Automated summary

The study focuses on the increasing demand of animal husbandry in arid areas in China and introduces a novel composite multifeature deep learning method for high-precision remote sensing recognition of mountainous grassland. By combining spectral bands with extracted features, the new method shows significant improvement in accuracy compared to traditional methods.