Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain sufficient labeled samples of polarimetric synthetic aperture radar (PolSAR) images. To address this problem, we propose a novel semi-supervised classification method for PolSAR images in this paper, using the co-training of CNN and a support vector machine (SVM). In our co-training method, an eight-layer CNN with residual network (ResNet) architecture is designed as the primary classifier, and an SVM is used as the auxiliary classifier. In particular, the SVM is used to enhance the performance of our algorithm in the case of limited labeled samples. In our method, more and more pseudo-labeled samples are iteratively yielded for training through a two-stage co-training of CNN and SVM, which gradually improves the performance of the two classifiers. The trained CNN is employed as the final classifier due to its strong classification capability with enough samples. We carried out experiments on two C-band airborne PolSAR images acquired by the AIRSAR systems and an L-band spaceborne PolSAR image acquired by the GaoFen-3 system. The experimental results demonstrate that the proposed method can effectively integrate the complementary advantages of SVM and CNN, providing overall classification accuracy of more than 97%, 96% and 93% with limited labeled samples (10 samples per class) for the above three images, respectively, which is superior to the state-of-the-art semi-supervised methods for PolSAR image classification.
In order to improve the accuracy and efficiency of airborne LiDAR point cloud data classification algorithm, a classification algorithm of point cloud based on LightGBM was proposed, and the classification effect of the algorithm on urban point cloud data was tested. In this paper, LightGBM-1 classifier was used to roughly classify point cloud data firstly. Then ground points were extracted to normalize non-ground points. After that, multi-scale neighborhood features of building points and vegetation points were extracted, and then building points and vegetation points were finely classified by LightGBM-2 classifier. The algorithm was verified by urban point cloud data, and the classification effect was evaluated by analyzing classification accuracy and time. Experimental results show that, compared with other algorithms, this algorithm can effectively improve the effect of point cloud data, and realize the effective classification of point cloud data in urban areas.
Urban laser radar point cloud building extraction is a hot spot in recent years, but the accurate distinction between vegetation, buildings and man-made objects has always been a difficult point. In this paper, a point cloud classification algorithm based on ICSF and weakly correlated random forest are proposed for the problem of low classification accuracy. Firstly, the data is ground-filtered by ICSF algorithm, then the decision tree is constructed, and correlation analysis is performed based on the maximum mutual information coefficient. A decision tree with the smallest correlation coefficient and the highest precision is selected to form a random forest. Finally, the decision results are weighted and completed. Point cloud classification. This paper validates the model through the Vaihingen city dataset and ranks the importance of the features according to the method of reducing the average precision. Compared with the traditional random forest classification algorithm, the classification accuracy is improved by 4.2%, which shortens the model time.
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