Tensor-Based Chaotic Convolutional Neural Network for Remote Sensing Data Classification

With the advancement of deep learning techniques, the classification of remote sensing data using artificial neural networks has emerged as a prominent research area. Despite this progress, the emulation of brain structures by traditional artificial neural networks remains at a relatively low level. Therefore, there is an urgent need to explore novel methods that can more effectively simulate human brain functions and enhance algorithmic performance. At the same time, artificial neural networks in image processing require the transformation of raw data into vector form. However, this conversion disrupts the inherent relationships between adjacent positional data in the raw data, resulting in the loss of crucial spatial structural information. To address these issues, we propose a tensor-based chaotic convolutional neural network model for the classification of remote sensing data. Firstly, we constructed a 3D discrete chaotic system, utilizing both Logistic mapping and Tent mapping, to achieve a more uniform iterative distribution and a broader full mapping range. This system was then integrated with convolutional neural networks to devise a novel chaotic convolutional neural network algorithm. By introducing chaotic mechanisms, this algorithm addresses the drawback of neural networks being prone to local minima. Secondly, we established a tensor model for remote sensing data. In contrast to existing methods that convert raw data into vector form, our approach represents raw data in tensor form, thereby preserving the spatial structural information among the three channels of the raw data. Finally, the effectiveness of the algorithm was validated using the NWPU-RESISC45 remote sensing dataset