Dr Al Adil Al Hinai

In this paper, a novel set of 13 handcrafted features derived from the contours of ships in synthetic aperture radar (SAR) images is introduced for ship classification. Additionally, the information entropy is presented as a valuable metric for quantifying the confidence (or uncertainty) associated with classification predictions. Two segmentation methods for the contour extraction were investigated: a classical approach using the watershed algorithm and a U-Net architecture. The features were tested using a support vector machine (SVM) on the OpenSARShip and FUSAR-Ship datasets, demonstrating improved results compared to existing handcrafted features in the literature. Alongside the SVM, a random forest (RF) and a Gaussian process classifier (GPC) were used to examine the effect of entropy derivation from different classifiers while assessing feature robustness. The results show that when aggregating predictions of an ensemble, techniques such as entropy-weighted averaging are shown to produce higher accuracies than methods like majority voting. It is also found that the aggregation of individual entropies within an ensemble leads to a normal distribution, effectively minimizing outliers. This characteristic was utilized to model the entropy distributions, from which confidence levels were established based on Gaussian parameters. Predictions were then assigned to one of three confidence levels (high, moderate, or low), with the Gaussian-based approach showing superior correlation with classification accuracy compared to other methods.

This study investigates the amplitude artefacts induced by sublook decomposition (SD) in terrain observation by progressive scans synthetic aperture radar (TOPSAR) imagery and their implications for ship classification. The work presents a dual contribution: first, a theoretical framework is developed to derive the mechanism by which TOPSAR's inherent phase modulation is transformed into periodic amplitude artefacts during the SD process. Second, a novel, lightweight convolutional neural network, LeNet-4SD, is proposed to effectively leverage SD data. The model's architecture is distinguished by a multi-input design that processes azimuth sublooks, range sublooks, and the single look complex (SLC) amplitude image, through parallel branches fused by a spectrum-based attention mechanism. An evaluation on the OpenSARShip dataset demonstrates that LeNet-4SD achieves classification performance comparable to significantly larger benchmark models while being over two orders of magnitude more computationally efficient. To interpret the model's behavior, Shapley additive explanations was used to quantify the contribution of each data source. The analysis reveals distinct, class-specific dependencies, highlighting the model's reliance on sublook diversity for container ships and on the high-resolution SLC for tankers. These findings underscore the importance of accounting for processing-induced artefacts and establish that custom, lightweight, multi-input models can be a more effective and efficient strategy for synthetic aperture radar applications than fine-tuning large, pretrained networks.

In this paper, a novel algorithm for ship classification in Sentinel-1 synthetic aperture radar (SAR) images is presented. The algorithm utilises layover as the main classification feature, which is based on the different relative heights of superstructures in oil tankers, container ships, geared and gearless bulk carriers. The algorithm has been tested using 20 ship samples from Sentinel-1 stripmap images over the port of Santos, divided equally amongst the four ship classes. An overall classification accuracy of 75% has been achieved.