# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 2 Relevant Work
### Convolutional Neural Networks  
Assigning a semantic label to an image based on its content is known as natural scene classification, and it is a challenging task because of the variety and complexity of the remotely sensed images. According to [PERSON] et al. [5], deep-learning approaches, notably CNNs, have shown amazing performance in the natural scene classification of remote-sensing data. Several CNN architectures and approaches, including AlexNet, VGG-16, ResNet, transfer learning, and attention processes, have been suggested and used for this problem. These strategies have significantly increased classification accuracy while decreasing the quantity of training data necessary for natural scene categorization in remote-sensing pictures.  
AlexNet, introduced by [PERSON] et al. [6], is one of the oldest and most prominent CNN architectures for image classification. AlexNet won the ImageNet challenge and outperformed the competition on a variety of image classification benchmarks. Since then, AlexNet has been widely employed in a variety of applications, including natural scene categorization in remote-sensing data. [PERSON] et al. [23], for example, provided a multi-sensor data-fusion framework for natural scene categorization in which AlexNet was utilized as the classification model to combine information derived from various sensors.  
Several deeper and more complicated CNN architectures have been suggested and utilized for natural scene categorization in remote-sensing images since AlexNet. For example, VGG-16 and ResNet have been employed to increase the classification accuracy on various datasets. VGG-16, which features 16 convolutional layers and a considerably deeper network architecture than AlexNet, has demonstrated an enhanced performance on natural scene categorization tests. To solve the issue of disappearing gradients in deep networks, ResNet incorporated residual connections between layers. This method allows the network to be trained in greater depth without losing performance. [PERSON] et al. [8] presented VGG-Net, a very deep convolutional neural network architecture. The authors described the network's architecture, which consists of multiple convolutional layers, achieving excellent performance on the ImageNet dataset and establishing a new benchmark in image classification [8; 15].  
[PERSON] et al. [24] examined deep-learning strategies for remotely sensed image scene categorization, with a special focus on feature extraction and classification algorithms. To capture both spatial and temporal information in remote-sensing data, the authors devised a hybrid deep-learning architecture that integrates CNNs and RNNs. [PERSON] et al. [25] built on their earlier work by developing a unique attention-based deep-learning model for remote-sensing picture scene categorization. The scientists added an attention mechanism to the CNN design to selectively focus on relevant portions of the input picture, increasing the model's discriminative strength. [PERSON] et al. [26] suggested a feature-fusion method for classifying remotely sensed images that incorporated spectral and textural data using a CNN. The suggested technique outperformed standard feature extraction methods in terms of performance.  
Unfortunately, in these available techniques, transfer learning is not exploited well because of the scarcity of pre-trained models. If used rarely, it results in constrained performance in-domain transfer learning. Unfortunately, the employment of cross-domain transfer learning is also rare because of the risk of low performance in nature scene classification. Here, we go for it.