Scene Classification of High-Resolution Remotely Sensed Image Based on ResNet

Remote sensing technology for earth observation is becoming increasingly important with advances in economic growth, rapid social development and the many factors accompanying economic development. High spatial resolution remote sensing images come with distinct layers, clear texture and rich spatial information, and have broad areas of application. Deep learning models have the ability to acquire the depth features contained in images but they usually require a large number of training samples. In this study, we propose a method to realize scene level classification of high spatial resolution images when a large number of training samples cannot be provided. We extracted the depth features of high-resolution remote sensing images using a residual learning network (ResNet), and low-level features, including color moment features and gray level co-occurrence matrix features. We used these to construct various scenes semantic features of high-resolution images, and created a classification model with the training support vector machine (SVM). According to the sample migration method, with the UC Merced Land Use (UCM) data set as the migration sample, a scene classification accuracy of GF-2 data set can reach 95.71% with a small sample size. Finally, through this method, GF-2 image scene level classification is implemented in line with reality.