###### Abstract
Remote sensing scenes include large variations in object/scene scales and the coexistence of multiple ground objects. As illustrated, remote sensing scenes exhibit high intraclass diversity and interclass similarity. Researchers have long been accustomed to improving performance based on backbone networks pretrained on ImageNet, but ImageNet pretraining may, in fact, limit the performance of methods based on it. To explore possible solutions to address the decrease in accuracy caused by model training without ImageNet pretraining, we propose a general framework to help train various CNNs using only remote sensing images. Accordingly, we answer the previous questions as follows: ImageNet pretraining is not necessary, and state-of-the-art results can be achieved using only remote sensing images. The proposed framework comprises three steps, as shown in Figure 3: (1) self-supervised learning for weight initialization, (2) learning specific data augmentation strategies, and (3) training under regularization strategy. Our experiments suggest that our proposed general framework for RSSC (GFSC) allows CNNs to achieve comparable state-of-the-art results when trained only on RSSC datasets. At the same time, the models trained under GFSC can \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Classes** & **Total Images** & **Images/Class** \\ \hline _Natural Image Datasets:_ & & & \\ \hline ImageNet (2012) [19] & 1000 & 1,431,167 & \(\sim\)1000 \\ MNIST [20] & 10 & 60,000 & 6000 \\ CIFAR-10 [21] & 10 & 60,000 & 6000 \\ \hline _RSSC Datasets:_ & & & \\ \hline NWPU-RESISC45 [22] & 45 & 31,500 & 700 \\ AID [23] & 30 & 10,000 & 200–420 \\ PatternNet [24] & 38 & 30,400 & 800 \\ UC-Merced [25] & 21 & 2100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of RSSC datasets and natural image datasets. Figure 2: Comparison of classification accuracy with ImageNet pretraining and without ImageNet pretraining. extract more discriminative features than the models using ImageNet pretraining. We note that almost all individual components of our framework have appeared in previous work, although their specific implementations may be different. The superiority of our framework relative to previous work is not explained by any single design choice, but rather by their combination. The main contributions of this paper are as follows:  
1. We propose a general training framework, GFSC, for training RSSC models without ImageNet pretraining. This framework can achieve results that surpass those of methods based on ImageNet pretraining using only remote sensing images. 2. Compared with ImageNet pretraining, GFSC enables the extraction of more discriminative features with less consumption of computational resources. 3. Our proposed framework is easy to implement, exhibits good generalizability to different CNN structures, and yields consistent performance improvements. The rest of this paper is organized as follows. Section 2 reviews the related work of this paper. Then, the proposed GFSC is introduced in Section 3. Experiments conducted to test the proposed method are described in Section 4. Finally, we conclude this article in Section 5.