土地利用分类粒子群优化概率神经网络半监督算法

针对以往土地利用监测大都采用监督分类算法,成本较高、错分漏分严重且受人为因素影响较大的问题,提出了一种粒子群优化概率神经网络的半监督分类算法.该算法通过粒子群优化算法优化分类器的参数,提高分类器的精度,运用香农熵选择高置信度的样本扩展初始训练样本集,将大量未标记的样本扩展到训练样本集中,减少了初始标签样本的数量,节约了...

Semi-supervised Land Use Classification Based on Particle Swarm Optimization Probabilistic Neural Network

Aiming at the problem that most of the land use monitoring in the past uses supervised classification algorithms, which have high costs, wrong leakage points, and greatly affected by human factors, a semi-supervised classification algorithm was proposed for particle swarm optimization probability neural networks, which improved the classification accuracy. The algorithm optimized the parameters of the classifier through the particle swarm optimization algorithm, improved the accuracy of the classifier, and Shannon entropy was used to select high-confidence samples to expand the initial training sample set, a large number of unlabeled samples were expanded to the training sample set, the number of initial label samples were reduced, costs were saved, and it was compared and analyzed with random forest, maximum likelihood method, and probabilistic neural network algorithm, the classification accuracy was improved by 1.25~6.57 percentage points compared with that of other algorithms, and the Kappa coefficient reached more than 0.8. Through the land classification of the remote sensing images of Xinxiang City in 1996, 2004, 2013 and 2020, the results showed that the construction land of Xinxiang City from 1996 to 2020 was continuously expanded in Xinxiang County in the central region, and the cultivated land area was also increased, and the area of other land used was decreased, and the area along the Yellow green area was increased; the land circulation was the most obvious for the conversion of cultivated land to construction land. The research results provided a certain reference for the further rational development of land resources in Xinxiang City.