Application of convolutional neural network in fusion and classification of multi-source remote sensing data

Through remote sensing images, we can understand and observe the terrain, and its application scope is relatively large, such as agriculture, military, etc.

In order to achieve more accurate and efficient multi-source remote sensing data fusion and classification, this study proposes DB-CNN algorithm, introduces SVM algorithm and ELM algorithm, and compares and verifies their performance through relevant experiments.

From the results, we can find that for the dual branch CNN network structure, hyperspectral data and laser mines joint classification of data can achieve higher classification accuracy. On different data sets, the global classification accuracy of the joint classification method is 98.46%. DB-CNN model has the highest training accuracy and fastest speed in training and testing. In addition, the DB-CNN model has the lowest test error, about 0.026, 0.037 lower than the ELM model and 0.056 lower than the SVM model. The AUC value corresponding to the ROC curve of its model is about 0.922, higher than that of the other two models.

It can be seen that the method used in this paper can significantly improve the effect of multi-source remote sensing data fusion and classification, and has certain practical value.