基于多维多粒度级联森林的高原地区云雪分类

针对传统算法如支持向量机（SVM）、随机森林不能充分利用卫星图像的纹理特征和光学参数的问题，提出一种基于多维多粒度级联森林（M-gcForest）的方法进行准确又快速的云雪识别。首先，根据单光谱和多光谱图像之间的差异性，选择SVM、随机森林、卷积神经网络（CNN）、多粒度级联森林（gcForest）在单光谱卫星图像上进行云雪识别；然后，通过定量分析各算法在单光谱图像上的性能，选择CNN和M-gcForest进行多光谱云雪识别；最后，利用改进的M-gcForest对HJ-1A/1B多光谱卫星图像进行预测。实验结果表明，与CNN相比，M-gcForest在多光谱数据集上的测试准确率提升了0.32%，训练耗时减少了91.2%，测试耗时减少了53.7%。因此，该算法在实时而准确的雪灾监测任务中具有实用性。

Cloud/Snow classification based on multi-dimensional multi-grained cascade forest in plateau region

To solve the problem that the traditional algorithms, such as Support Vector Machine (SVM) and random forest, cannot make full use of the texture features and optical parameters of satellite images, a method of cloud/snow recognition based on Multi-dimensional multi-grained cascade Forest (M-gcForest) was proposed. Firstly, according to the difference between single-spectral and multi-spectral images, SVM, random forest, Convolution Neural Network (CNN), and gcForest (multi-grained cascade Forest) were selected to recognize cloud and snow on single-spectral satellite images, by quantitatively analyzing the performance of each algorithm on single-spectral images, CNN and M-gcForest were selected for multi-spectral cloud/snow recognition. Finally, improved M-gcForest was used to predict on HJ-1A/1B multi-spectral satellite images. The experimental results show that compared with CNN, the test accuracy of the M-gcForest on the multi-spectral dataset is increased by 0.32%, the training time is reduced by 91.2%, and the testing time is reduced by 53.7%. Therefore, the proposed algorithm has practicability in real-time and accurate snow disaster monitoring tasks.