Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm

In recent years, high spatial resolution remote sensing technology has made significant progress. High-resolution remote sensing satellites provide great convenience for high-quality image acquisition. In order to adapt to changes in the appearance of the target, mainstream tracking algorithms often use pattern recognition methods to build a target appearance model with learning capabilities, and use the image frames acquired during the tracking process to update the appearance model. This paper mainly studies the object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm. In this paper, we use the splitting mechanism of the tree structure to retain the appearance model with diversity, and through the integrated learning integration strategy, the target position is collaboratively predicted. Through the comparative analysis on the OTB and VOT platforms, the algorithm works well when the requirements of the tracking standards are low (the accuracy threshold is greater than 20 pixels and the success threshold is less than 0.4 pixels). The experimental results in this paper show that compared with other advanced classification methods, the proposed method shows better generalization performance in accuracy, recall, f-measure, g-mean and AUC.