# Satellite Image Categorization Using Scalable Deep Learning
## 5 Discussion  
The proposed methodology presents the automated interpretation of satellite images. Manual interpretation can be very laborious work. Furthermore, domain expertise is required for the accomplishment of such a task. The proposed solution can classify the  
Figure 8: Loss-accuracy curves for training and validation: (**a**) Inception Net; (**b**) MobileNet; (**c**) ResNet; (**d–g**) EfficientNet B4–B7.  
images into four different classes. Different state-of-the-art techniques are employed for the said problem. It is evident from the results that EfficientNet B7 provides the best results.  
Deep learning is a sub-field of machine learning capable of representing and analyzing data at different abstraction levels. It has achieved very astonishing results in the field of satellite imaging and geoscience. This research experiments with various deep learning models for satellite image interpretation and classification. These models include Inception V3, ResNet, MobileNet, EfficientNet B4, EfficientNet B5, EfficientNet B6, and EfficientNet B7. EfficientNet B7 gives the highest accuracy, F1 score, and ROC AUC. ResNet, a non-scalable network, also gives very promising results. In comparison, Inception Network was unable to give very encouraging results. Inception Network distributes feature extraction processing between scaling and merging, while ResNet provides uni-scale processing and pass-through connections. Inception, ResNet, and MobileNet are convolution-based neural networks that serve for the feature extraction and classification of images. Inception Networks can provide cost-effective solutions for larger data. In contrast to Inception Network, ResNet focuses on delivering a good accuracy.  
In some cases, deeper neural networks can face difficulties due to vanishing or exploding gradients, overfitting, and computational complexity. When dealing with limited or noisy data, very deep networks may struggle to learn meaningful representations and require significant computational resources for training. Furthermore, deeper networks may be more prone to overfitting, particularly when the dataset is small or the model is overly complex. Therefore, it is crucial to carefully weigh the trade-offs between depth and performance when designing neural networks for specific tasks and datasets. ResNet can deal better with vanishing gradient problems. This is because it facilitates the smooth flow of gradients through complex neural networks, thereby enabling the training of deeper networks and enhancing convergence properties. MobileNet also gives acceptable results with a 0.949 accuracy, a 0.951 F1 score, and a 0.997 AUC value. MobileNets use depth-wise separable convolutions that help in creating lighter networks with fewer resource requirements. MobileNets use regular convolutions for the first layer but use separable convolutions for the rest of the layers.  
EfficientNets employ the compound scaling behavior. It provides width, depth, and resolution scalability. A wider net applies more feature maps during the convolution process; deeper networks can employ more layers, while high-resolution processing means feature maps with higher spatial dimensions. Apart from salability, EfficientNet has 3.8\(\times\) fewer parameters and FLOPs than ResNet. EfficientNet B7 outperforms all the other architectures in the test results by giving a 0.996 accuracy, a 0.997 F1 measure, a 0.995 Cohen's kappa score, and an ROC AUC value of 0.999. The feature embeddings also show that EfficientNets have the most separable embeddings, with less overlapping areas.