# Satellite Image Categorization Using Scalable Deep Learning
## 4 Results
### Experimental Results  
The proposed methodology is tested on the RSI-CB256 dataset. The results of other popular architectures are also reported using the same dataset. Table 2 shows the detailed results of different architectures on the aforementioned dataset. EfficientNets in general, and EfficientNet-B7 in particular, has the ability to classify the satellite image data. EfficientNet B7 reports the highest accuracy amongst all architectures. However, it is important to note that accuracy alone may not be the best metric for evaluating the performance of a machine learning model, especially in cases where the classes are imbalanced or there are multiple classes. Looking at the evaluation metrics, it appears that EfficientNet B6 and B7 perform very well across all metrics, with high values for accuracy, precision, recall, F1 score, Cohen's kappa, and ROC AUC. This indicates that these models are able to effectively classify the input images and maintain high levels of consistency in their predictions. The EfficientNet models are also compared with the ResNet-50 [50], Inception V3 [51], and MobileNet [52] models. ResNet also performs very well, with high values for precision, recall, F1 score, and Cohen's kappa, as well as a perfect ROC AUC score. This suggests that ResNet is able to correctly classify both positive and negative instances with a high accuracy, and it is also able to balance precision and recall well. Inception V3 and MobileNet show slightly lower scores than ResNet and the EfficientNet models. An important observation is that Inception V3 exhibits the lowest [PERSON]'s kappa score among the compared models, suggesting that it may exhibit a greater variability in its predictions than the other models, potentially indicating less consistency in its performance. Figure 6 shows the confusion matrix for all the methodologies. A confusion matrix is also known as an error matrix. It shows the statistical classification results of the proposed methodology. The horizontal strips correspond to the actual classes, while the vertical pillars showcase the predicted classes. The confusion matrix of EfficientNet shows that most images are classified accurately with fewer classification errors. The EfficientNet B7 model correctly predicts all 150 instances of the \"Cloudy\" class (TP = 150, FP = 0, and FN = 0), all 114 instances of the \"Desert\" class (TP = 114, FP = 0, and FN = 0), and all 150 instances of the \"Green_area\" class (TP = 150, FP = 0, and FN = 0), resulting in a perfect precision and recall for these classes. The model incorrectly predicts 1 instance of the \"Water\" class as \"Cloudy\" and 1 instance as \"Green_area\" (TP = 148, FP = 1, and FN = 1), resulting in a slightly lower precision and recall for this class. ResNet also performs well with most of the images. Figure 7 shows the feature embedding in the 2-D mapping of all the architectures. T-SNE is used for the visualization of the embeddings. T-SNE is capable of visualizing trends in data in spaces of lower dimensions. T-SNE is a dimensionality reduction technique commonly used for visualizing high-dimensional data. It is particularly useful for visualizing feature vectors because it can reveal the underlying structure of the data in a lower-dimensional space. The basic idea of T-SNE is to transform high-dimensional feature vectors into a low-dimensional space (typically 2D or 3D) while preserving the pairwise similarities between the data points as much as possible. It does this by defining a probability distribution over pairs of high-dimensional points and a similar probability distribution over pairs of corresponding low-dimensional points. The algorithm then tries to minimize the difference between these two distributions using gradient descent. One of the key advantages of T-SNE is that it can reveal non-linear relationships in the data that may not be apparent using other methods. For example, it can help to distinguish between  
clusters of similar data points, even if those clusters overlap in high-dimensional space.