## 3 Materials and Methods
### The FRCNN Method
Initially, the SAR input image is taken from the dataset and output such as class probability and bounding box  
Figure 4: Proposed FRCNN model. set. Algorithm 1 procedure consists of computational functions such as RGB calculation of GAN and conversion of color space followed by LUV channel. The final LUV format image calculates magnitude and orientation. Then, the final image LUV is checked for ships or icebergs with a confidence score of region of proposal (ROP). This FRCNN must not be dropped to detect even small images, and the ROP confidence score is set at greater than 50% onwards with aggregate channel [21]. The next step of this FRCNN is to generate a feature map with ResNet with its appropriate layers. Next, the feature vectors are extracted for the image of LUV format with ROP of ACF, which underwent ResNet 50. Finally, it can find the probability and bounding box for inputting SAR images to detect ships and icebergs. ``` Input: SAR Image I, Dataset Output: Class Probability, Bounding Box Procedure: For g = 1 to Ng where Ng is Number of image generated by GAN Calculate RGB channel for GAN[g] Convert RGB channel into Colors space Convert color space into LUV channel Calculate Magnitude and Orientation of LUV image of m x n Mag(i,j) = \(\sqrt{(\partial\)GAN[g](i,j)/\(\partial\)x)2 + \((\partial\)GAN[g](i,j)/\(\partial\)y)2) Ori(i,j) = tan-1((\(\partial\)GAN[g](i,j)/\(\partial\)x)/\(\partial\)GAN[g](i,j)/\(\partial\)x)) Do Convolution of Magnitude Image Do Normalization of Convoluted Image Compute HoG and Aggregate with LUV image If (Confidence > 50) extract ROP from Aggregate channel // FRCNN working mechanism Generate Feature Map for Input Image I using RESNET convolutional and Maxpool layer Extract feature vector for each ROP of ACF Input fixed size vector to fully connected layers of RESNET 50 Calculate discrete probabilities of all region proposals P = {p0, p1, p2, p3... pk}. bb= {bbx, bby, bbw, bbb} Calculate Lossclass (P, T) = -log PT which is log loss for true class T.  Lloc (bbpred,T) = \(\sum\) smoothloc (bbpredi-Ti) \(\text{smooth}_{\text{loc}}\) (x) = \( \begin{cases}0.5x*x\,if\,|x|<1\\ |x|-0.5\,otherwise\end{cases} \) and bbbpred is predicted set of bounding box like {bbpredx, bbpredy, bbpredw, bbpredh End for For all ROI of I

## 4 Experimental Result

The proposed algorithm was implemented in MATLAB. The C-core dataset was used in the implementation [3]. The images of the ship and iceberg were stored as JSON files. These four fields are in a JSON file. The ID of the image, band one and band 2 data flattened as a numerical value, incidence angle, and the last filed refer to whether the given ROI is an iceberg. If the field contains 0, it shows it is a ship; if it has 1, it is an iceberg. The images in the dataset are in 75 \(\times\) 75 sizes and have two bands. The expert team gives the labels. The implementation started with augmenting images by using GAN architecture. Figure 6, left side, demonstrates the sample output of augmented images. Figure 6 on the right side shows the training fashion and the generator and discriminator scores. Then, the experiment was continued with an ACF detector where each augmented image color channel and histogram of the gradient had to be calculated. Figure 7 shows the sample output of the HoG of the SAR image. After calculating the aggregation of all channels, the ACF detector finds the region proposal with the help of the confidence score. Figures 8 and 9 show the output of the ACF detector. The inference was that a ship or iceberg was detected if the confidence score gained more than 50% as per the computation of Algorithm 1 with the guidance of ACF. Figure 5: Proposed Framework’s Architecture Diagram. Figure 8: Output of ACF detectors. Figure 6: Generated Image and Training Fashion. Figure 7: HoG of SAR image. Then, fast R-CNN started with the region proposals of ACF detectors with the help of ResNet 50 backbone architecture. Finally, the bounding box located the object with classifications labels, whether an iceberg or a ship.