Lightweight SAR Ship detection and 16 Class Classification using Novel Deep Learning Algorithm with a Hybrid Preprocessing Technique

Abstract

ABSTRACT Many studies using deep learning methods for automatic ship detection from SAR images have good detection accuracy. Researchers are mainly focused on classifying large ships with distinct features like tankers, cargo vessels, and container ships. So, more research is needed for the classification of the seen ships into subclasses. Deep learning-based complete full-fledged ship detection and classification is challenging because of the unavailability of SAR data sets with localization details and class information (sub-class details). This paper proposes a ship detection and classification system for classifying the seen ships into 16 classes. In this method, the 2D SAR data are preprocessed to generate ’SarNeDe’ data using image processing and despeckling techniques to increase accuracy by reducing false predictions and wrong classifications. This ’SarNeDe’ is used to train and test the deep learning model for detection and classification. This model is designed based on a two-stage object detection style without compromising the speed and accuracy. The detection part (L-model), developed mostly using depthwise separable CNNs with multi-scale and multianchor box detection schemes, estimates the accurate position of all ships in the SAR data. These seen ships’ SarNeDes data are given as input to the classification part (C-model), based on a one-shot learning-based classification technique built from scratch to classify 16 ship classes. Detection experimental results on the public SAR ship detection data set (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) and experimental classification results in OpenSARShip data set for ship classification validate the proposed method’s feasibility. The proposed system is lightweight and can be used in real-time applications.