# Satellite Image Categorization Using Scalable Deep Learning
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.3 Feature Extraction  
EfficientNets are a kind of convolutional neural network that works on the principle of scaling [49]. EfficientNets are used as feature extractors in the presented research. The EfficientNet architecture consists of three components: the stem, the blocks, and the head. The stem is the initial part of the network that processes the input image, while the blocks are repeated units of convolutional layers that learn increasingly complex features. The head is the final part of the network that maps the learned features to the output classes. They are designed to provide state-of-the-art performance in image classification tasks while being computationally efficient. These adjust the architecture's width, resolution, and depth with compound coefficients \(\phi\). This scaling is very effective, as larger input images require more complicated architectures to compute results with more channels and layers to analyze fine-grained patterns. Compound coefficients \(\phi\) evenly adjust the width (\(\omega\)), resolution (\(\Re\)), and depth (\(\Im\)) of the underlying architecture, where  
\[\Im=\ \bar{x}^{\phi} \tag{10}\]  
\[\omega=\eta^{\phi} \tag{11}\]\[\Re=\textit{s}^{\phi} \tag{12}\]  
\[\therefore\ \mathfrak{X}.\ \mathfrak{\eta}^{2}.\textit{s}^{2}\approx 2\]  
\[\mathfrak{X}\succcurlyeq 1,\ \mathfrak{\eta}\succcurlyeq 1,\ \textit{s}\succcurlyeq 1\]  
\(\phi\) is the input taken from the user, and \(\mathfrak{X},\ \mathfrak{\eta},\ \textit{s}\) specifies the correlation with the network width, resolution, and depth. The value of \(\phi\) affects the width, depth, and resolution of the network. When \(\phi\) is greater than 1, the network size increases, leading to a wider, deeper, and higher resolution. Conversely, when \(\phi\) is less than 1, the network size decreases, resulting in a narrower, shallower, and lower resolution. The values of \(\phi\) used in this research for EfficientNet B4-B7 are 1.8, 2.0, 2.2, and 2.4, respectively. \(\mathfrak{X},\ \mathfrak{\eta},\ \text{and}\ \mathfrak{\mathfrak{\mathfrak{ \mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{    }}}}}}}}}}}}}}\) can be computed through a minor grid search. A minor grid search is a process of finding the optimal values for the coefficients \(\mathfrak{\eta},\ \mathfrak{X},\) and \(\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{ \mathfrak{ \mathfrak{      }}}}}}}}}}}}}}}\) so that the constraint \(\mathfrak{X}.\ \mathfrak{\eta}^{2}.\textit{s}^{2}\approx 2\) is satisfied. This constraint ensures that the computational cost of the model remains roughly constant as these coefficients are adjusted. The equations involving the coefficients \(\phi\) adjust the architecture's width, resolution, and depth. Specifically, \(\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{\mathfrak{\mathfrak{\mathfrak{\mathfrak{ \mathfrak{ \mathfrak{ \mathfrak{ \mathfrak{ \mathfrak{        \mathfrak{ \mathfrak{         }}}}}}}}}}}}}}\) is the depth, \(\omega\) is the width, and \(\Re\) is the resolution. The equations show that \(\phi\) is raised to the power of these values to adjust them. EfficientNet proposes better and more accurate results via the compound scaling of these three network parameters. However, this will slightly increase the floating-point operations per second (FLOPS) by \(\left(\mathfrak{X}.\textit{p}^{2}.\textit{s}^{2}\right)^{\phi}\). However, \(\mathfrak{X}.\ \mathfrak{\eta}^{2}.\textit{s}^{2}\approx 2\), so FLOPS are increased approximately by 2\({}^{\phi}\) only [49].