# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 2 Relevant Work
### Deep Learning  
[PERSON] et al. [10] introduced the concept of deep learning and its applications in various domains, including computer vision. The authors discussed the fundamental principles of deep learning and its potential in solving complex problems by automatically learning hierarchical representations from data. [PERSON] et al. [4] conducted a comprehensive survey on remotely sensed image scene categorization using deep-learning algorithms. The authors reviewed the difficulties encountered in remote-sensing picture scene categorization and offered an outline of the various deep-learning approaches used in this sector. They also reviewed benchmark datasets used to evaluate the performance of classification systems and highlighted future research prospects in this field. [PERSON] et al. [11] provided a benchmark and up-to-date review of remote-sensing picture categorization. They talked about several classification strategies, such as classic  
Figure 1: VGG-16 Architecture. ML and DL methods. The authors assessed the performance of different approaches on benchmark datasets and discussed their advantages and disadvantages. The AID dataset, developed by [PERSON] et al. [12], serves as a baseline for testing the performance of aerial scene categorization systems. The authors thoroughly presented the dataset, including scene categories and picture attributes, and emphasized its significance in furthering research in aerial scene categorization. [PERSON] et al. [13] developed a flexible segmentation graph-based multi-dimensional contextual method for scene annotation. The authors proved the method's performance on multiple datasets, demonstrating its potential for properly categorizing scenes based on contextual information. [PERSON] et al. [14] presented a method for learning deep features that are discriminative for object localization in images. The authors described how their approach enables the network to focus on relevant object details, leading to improved object localization performance. [PERSON] et al. [15] presented deep residual networks and established the notion of residual learning. Their research proved that by exploiting residual connections, deeper networks may be trained more successfully, resulting in superior performance in image classification tasks. [PERSON] et al. [16] suggested a method for scene parsing that makes use of inference-embedded deep networks. The authors described how their method incorporates inference steps within the network architecture, enabling efficient scene parsing. They evaluated the effectiveness of their approach on the Pascal VOC dataset, demonstrating accurate scene-parsing results. [PERSON] et al. [17] proposed using full-resolution residual networks to separate street scenes for semantic segmentation. Their strategy outperformed competitors on the Cityscapes dataset, demonstrating the utility of full-resolution processing in semantic segmentation tasks. [PERSON] et al. [18] introduced an adaptive deep co-occurrence feature-learning strategy for remote-sensing scene categorization based on classifier fusion. The scientists revealed how they used deep-learning features and co-occurrence matrices to capture both spectral and spatial information in remote-sensing pictures, resulting in enhanced classification accuracy. [PERSON] et al. [19] suggested a technique for remote-sensing scene classification combining CNN-based features and a deep forest classifier. The authors highlighted the advantages of their approach, including its ability to handle high-dimensional data and its efficiency in training and classification. [PERSON] et al. [20] investigated the application of generative adversarial networks (GANs) for visual scene categorization in remote sensing. The authors suggested a GAN-based framework that uses the adversarial training process to produce synthetic samples, which are then mixed with actual examples to improve the classification model's diversity and generalization capabilities. [PERSON] et al.