Conference Papers
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Item Comparative Analysis of Machine Learning and Deep Learning Models for Ship Classification from Satellite Images(Springer Science and Business Media Deutschland GmbH, 2022) Hazarika, H.; Jidesh, P.; Smitha, A.The automatic detection of the ship from satellite image analysis is the limelight of research in recent years due to its widespread applications. In this paper, a handful of traditional machine learning and deep learning models are compared based on their performance to classify the satellite images available in the public repository as a ship or other categories. The Support Vector Machine(SVM), Decision Trees, Random Forest, K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GaussianNB), and Logistic Regression are machine learning models used in the present work. Histogram of Gradient (HoG) features are used as feature descriptors considering the diverse size and shape of ships in the satellite image dataset. Transfer learning is applied using the deep learning models namely, Inception and ResNet, that are fine-tuned for various learning rates and optimizers. The meticulous experimentation carried out reveals that traditional machine learning performs well when trained and tested on a single dataset. However, there is a drastic change in the performance of machine learning models when tested on a different ship dataset. The results show that the deep learning models have better feature detection and thus have better performance when transfer learning is used on various datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Detection and Classification of Ships using a self-attention Residual Network(Institute of Electrical and Electronics Engineers Inc., 2022) Hazarika, H.; Jidesh, P.The categorization and detection of ships is crucial in maritime applications such as marine surveillance, traffic monitoring etc., which are extremely crucial for ensuring national security. There has been extensive amount of research happening in this area since the last decade. The acceleration of computational power and increasing number of satellite images collected over the years paved the way for such an extensive research. However, high-resolution images of ships that capture the texture details for an effective categorization are still very limited. In the recent years, researchers collected images over various sources to create datasets which are made publicly available for use. In our work, we use the ShipRSImageNet dataset which was recently released. Many state-of-the-art object detection methods were employed on this dataset for different levels of ship detection. But due to the complexity of the dataset, the state-of-the-art models are not capable enough to perform a full categorization of the ships in these dataset. In this work, a residual attention based convolutional neural network (CNN) model is employed for feature extraction, which can be fed in to the state-of-the-art object detection models for the extraction of the features. Usually, other backbone networks which can be employed are complex and consists of multiple layers. Due to the need for a large number of training parameters, these models and approaches have heavy-weight designs. Therefore, they need more storage and costlier training processes. © 2022 IEEE.