Ship Detection Feature Analysis in Optical Satellite Imagery through Machine Learning Applications

Ship detection remains an important challenge within the government and the commercial industry. Current research has focused on deep learning and has found high success with large labeled datasets. However, deep learning becomes insufficient for limited datasets as well as when explainability is required. There exist scenarios in which explainability and human-in-the-loop processing are needed, such as in naval applications. In these scenarios, handcrafted features and traditional classification algorithms can be useful. This research aims at analyzing multiple textures and statistical features on a small optical satellite imagery dataset. The feature analysis consists of Haar-like features, Haralick features, Hu moments, Histogram of Oriented Gradients, grayscale intensity histograms, and Local Binary Patterns. Feature performance is measured using 8 different classification algorithms, including K-Nearest Neighbors, Logistic Regression, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, Random Decision Forest, Extremely Randomized Trees, and Bagging. The features are analyzed individually and in different combinations. Individual feature analysis results found Haralick features achieved a precision of 92.2% and were computationally efficient. The best combination of features was Haralick features paired with Histogram of Oriented Gradients and grayscale intensity histograms. This combination achieved a precision score of 96.18% and an F1 score of 94.23%

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

State of the Art of Radar Images Recognition of Surface Ships by Means of Space Monitoring

Поступила: 01.02.2024. Принята в печать: 01.03.2024.Received: 01.02.2024. Accepted: 01.03.2024.Проблема синтеза и анализа алгоритмов обработки радиолокационных изображений пространственно-распределенных целей, полученных средствами космического мониторинга, была и остается одной из наиболее значимых как с теоретических, так и практических позиций для обеспечения безопасности мореплавания, контроля за незаконной добычей рыбы, мониторинга и управления кризисными ситуациями, такими как естественные бедствия, миграционные потоки и другие. Одним из наиболее распространенных приложений названной проблемы является распознавание надводных кораблей, которому и посвящен данный обзор, выполненный по иностранным источникам. В связи с этим предлагаемый обзор, содержащий достаточно подробный анализ современных методов решения названной задачи, предложенных широким кругом авторов в последние десятилетия, будет полезен создателям и исследователям средств космического наблюдения за состоянием морской поверхности.The issue of synthesizing and analyzing algorithms of processing radar images of spatially distributed targets, obtained through space monitoring tools, remains one of the most significant both theoretically and practically. This is particularly crucial for ensuring maritime safety, monitoring illegal fishing activities, and managing crisis situations such as natural disasters and migration flows. One of the most common applications of this problem is the recognition of surface ships, to which this review is devoted. The review is performed using foreign materials. Thus, the proposed review, which includes a detailed analysis of contemporary methods addressing the mentioned challenges, proposed by a wide range of authors over the past decades, will be valuable for developers and researchers in the field of space observation of marine surface conditions

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve

Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of food, they have a very large impact on the global environmental equilibrium, and it is over the oceans that most of the world commerce is done. Thus, maritime surveillance and monitoring, in particular identifying the ships used, is of great importance to oversee activities like fishing, marine transportation, navigation in general, illegal border encroachment, and search and rescue operations. In this thesis, we used images obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify what type of ship (if any) is present in a given location. Images generated from UAV cameras suffer from camera motion, scale variability, variability in the sea surface and sun glares. Extracting information from these images is challenging and is mostly done by human operators, but advances in computer vision technology and development of deep learning techniques in recent years have made it possible to do so automatically. We used four of the state-of-art pretrained deep learning network models, namely VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified their original structure using transfer learning based fine tuning techniques and then trained them on our dataset to create new models. We managed to achieve very high accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear on the images of our dataset. With such a high success rate (albeit at the cost of high computing power), we can proceed to implement these algorithms on maritime patrol UAVs, and thus improve Maritime Situational Awareness

Artificial Neural Networks and Evolutionary Computation in Remote Sensing

Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification