Remote sensing scene classification based on convolutional neural network model

In this context, Convolutional Neural Network (CNN) has quickly become the preferred method for remote sensing scene classification due to its excellent feature extraction and classification capabilities. Through its hierarchical structure and automatic feature learning process, CNN can effectively identify and classify different types of ground objects in large-scale and high-resolution remote sensing images. Its unique convolutional layer, pooling layer and fully connected layer architecture enable it to process complex image information, extract multi-scale and multi-level features, and achieve high-precision classification effects. We will introduce the CNN-based remote sensing scene classification method in detail, firstly describe the basic principles and architecture of CNN, and then discuss its specific applications and advantages in remote sensing image classification. At the same time, we will also analyze the main challenges faced in the practical application process, such as multi-scale feature extraction, data imbalance, and model generalization ability. Through the introduction of specific cases, the successful application of CNN in the fields of land use monitoring, environmental protection and urban planning is demonstrated.