High-Resolution Remote Sensing Image Scene Classification Using Star Kernel Feature Integration

In the classification of high-resolution remote sensing images, intricate scenes along with variations in lighting and viewpoint pose significant challenges for current convolutional neural networks to accurately capture essential features, leading to classification inaccuracies. The accurate categorization of these images is crucial for land management and environmental oversight. Thus, enhancing the model's resilience to guarantee dependable classification outcomes is an urgent issue that needs addressing. To this end, we propose a star-based multi-scale feature fusion network (StarFuNet), including StarFuNet-l, StarFuNet-b, StarFuNet-p, and StarFuNet-m. The model performs preliminary feature extraction and transformation operations on the input data by superimposing a four-layer EBlock module, which lays the foundation for subsequent processing. The feature fusion attention module is used to enhance the feature expression ability and improve the feature fusion effect, so that the model can better capture key information. The spatial pyramid pooling fast cross-stage partial connection module is used to transform the feature mapping of any size into a standardized feature vector to ensure that the model output has a high degree of adaptability and flexibility to adapt to different scales and types of data. By utilizing global average pooling, the normalized feature vectors are refined to produce highly precise classification outcomes, offering a dependable foundation for decision-making across numerous applications. We assessed the StarFuNet model using five publicly remote sensing datasets, and the findings indicate that its classification performance markedly surpasses that of current leading algorithms.