An Improved Pretraining Strategy-Based Scene Classification With Deep Learning

Abstract

High-resolution remote sensing (HRRS) image scene classification takes an important role in many applications and has attracted much attention. Recently, notable efforts have been made to present massive methods for HRRS scene classification, wherein deep-learning-based methods demonstrate remarkable performance compared with state-of-the-art methods. However, HRRS images contain complex contextual relationships and large differences of object scale, which are significantly different from natural images. The existing deep-learning-based scene classification methods are originally designed for natural image processing and have not been optimized to adapt to the characteristics of HRRS images, which significantly affects the efficiency of the feature extraction and recognition accuracy. In addition, when designing a model for remote sensing tasks, the pretraining of the model is time-consuming. The enormous amount of pretraining time and computation resources necessarily increase the difficulty of producing an excellent model. In this letter, focusing on the problems above, we proposed a new convolutional neural network (CNN)-based scene classification method. The CNN-based scene classification method is constructed by spatial-scale-aware blocks and is efficient in extracting the abundant spatial features, but can also adaptively adjust feature responses to maximize the function of informative features in the classification results. In addition, an HRRS imagery-based learning strategy is utilized to obtain an initial model for fine-tuning the model parameters, which drastically reduces the pretraining time. The proposed method has been demonstrated using two HRRS data sets, and experimental results have proven the superiority of the proposed method.