1,272 research outputs found
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%
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest
Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%
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
νλ ¨ μλ£ μλ μΆμΆ μκ³ λ¦¬μ¦κ³Ό κΈ°κ³ νμ΅μ ν΅ν SAR μμ κΈ°λ°μ μ λ° νμ§
νμλ
Όλ¬Έ (μμ¬) -- μμΈλνκ΅ λνμ : μμ°κ³Όνλν μ§κ΅¬νκ²½κ³ΌνλΆ, 2021. 2. κΉλμ§.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vesselβs movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner.
As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval.
Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.μ μ²ν μ§κ΅¬ κ΄μΈ‘ μμ±μΈ SARλ₯Ό ν΅ν μ λ° νμ§λ ν΄μ μμμ ν보μ ν΄μ μμ 보μ₯μ λ§€μ° μ€μν μν μ νλ€. κΈ°κ³ νμ΅ κΈ°λ²μ λμ
μΌλ‘ μΈν΄ μ λ°μ λΉλ‘―ν μ¬λ¬Ό νμ§μ μ νλ λ° ν¨μ¨μ±μ΄ ν₯μλμμΌλ, μ΄μ κ΄λ ¨λ λ€μμ μ°κ΅¬λ νμ§ μκ³ λ¦¬μ¦μ κ°λμ μ§μ€λμλ€. κ·Έλ¬λ, νμ§ μ νλμ κ·Όλ³Έμ μΈ ν₯μμ μ λ°νκ² μ·¨λλ λλμ νλ ¨μλ£ μμ΄λ λΆκ°λ₯νκΈ°μ, λ³Έ μ°κ΅¬μμλ μ λ°μ μ€μκ° μμΉ, μλ μ λ³΄μΈ AIS μλ£λ₯Ό μ΄μ©νμ¬ μΈκ³΅ μ§λ₯ κΈ°λ°μ μ λ° νμ§ μκ³ λ¦¬μ¦μ μ¬μ©λ νλ ¨μλ£λ₯Ό μλμ μΌλ‘ μ·¨λνλ μκ³ λ¦¬μ¦μ μ μνμλ€.
μ΄λ₯Ό μν΄ μ΄μ°μ μΈ AIS μλ£λ₯Ό SAR μμμ μ·¨λμκ°μ λ§μΆμ΄ μ ννκ² λ³΄κ°νκ³ , AIS μΌμ μμ²΄κ° κ°μ§λ μ€μ°¨λ₯Ό μ΅μννμλ€. λν, μ΄λνλ μ°λ체μ μμ μλλ‘ μΈν΄ λ°μνλ λνλ¬ νΈμ΄ ν¨κ³Όλ₯Ό 보μ νκΈ° μν΄ SAR μμ±μ μν 벑ν°λ₯Ό μ΄μ©νμ¬ μμ±κ³Ό μ°λ체 μ¬μ΄μ 거리λ₯Ό μ λ°νκ² κ³μ°νμλ€. μ΄λ κ² κ³μ°λ AIS μΌμμ μμ λ΄μ μμΉλ‘λΆν° μ λ° λ΄ AIS μΌμμ λ°°μΉλ₯Ό κ³ λ €νμ¬ μ λ° νμ§ μκ³ λ¦¬μ¦μ νλ ¨μλ£ νμμ λ§μΆμ΄ νλ ¨μλ£λ₯Ό μ·¨λνκ³ , μ΄μ μ λν μμΉ, μλ μ λ³΄μΈ VPASS μλ£ μμ μ μ¬ν λ°©λ²μΌλ‘ κ°κ³΅νμ¬ νλ ¨μλ£λ₯Ό μ·¨λνμλ€.
AIS μλ£λ‘λΆν° μ·¨λν νλ ¨μλ£λ κΈ°μ‘΄ λ°©λ²λλ‘ μλ μ·¨λν νλ ¨μλ£μ ν¨κ» μΈκ³΅ μ§λ₯ κΈ°λ° μ¬λ¬Ό νμ§ μκ³ λ¦¬μ¦μ ν΅ν΄ μ νλλ₯Ό νκ°νμλ€. κ·Έ κ²°κ³Ό, μ μλ μκ³ λ¦¬μ¦μΌλ‘ μ·¨λν νλ ¨ μλ£λ μλ μ·¨λν νλ ¨ μλ£ λλΉ λ λμ νμ§ μ νλλ₯Ό 보μμΌλ©°, μ΄λ κΈ°μ‘΄μ μ¬λ¬Ό νμ§ μκ³ λ¦¬μ¦μ νκ° μ§νμΈ μ λ°λ, μ¬νμ¨κ³Ό F1 scoreλ₯Ό ν΅ν΄ μ§νλμλ€. λ³Έ μ°κ΅¬μμ μ μν νλ ¨μλ£ μλ μ·¨λ κΈ°λ²μΌλ‘ μ»μ μ λ°μ λν νλ ¨μλ£λ νΉν κΈ°μ‘΄μ μ λ° νμ§ κΈ°λ²μΌλ‘λ λΆλ³μ΄ μ΄λ €μ λ νλ§μ μΈμ ν μ λ°κ³Ό μ°λ체 μ£Όλ³μ μ νΈμ λν μ νν λΆλ³ κ²°κ³Όλ₯Ό 보μλ€. λ³Έ μ°κ΅¬μμλ μ΄μ ν¨κ», μ λ° νμ§ κ²°κ³Όμ ν΄λΉ μ§μμ λν AIS λ° VPASS μλ£λ₯Ό μ΄μ©νμ¬ μ λ°μ λ―Έμλ³μ±μ νμ ν μ μλ κ°λ₯μ± λν μ μνμλ€.Chapter 1. Introduction - 1 -
1.1 Research Background - 1 -
1.2 Research Objective - 8 -
Chapter 2. Data Acquisition - 10 -
2.1 Acquisition of SAR Image Data - 10 -
2.2 Acquisition of AIS and VPASS Information - 20 -
Chapter 3. Methodology on Training Data Procurement - 26 -
3.1 Interpolation of Discrete AIS Data - 29 -
3.1.1 Estimation of Target Interpolation Time for Vessels - 29 -
3.1.2 Application of Kalman Filter to AIS Data - 34 -
3.2 Doppler Frequency Shift Correction - 40 -
3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 -
3.2.2 Mitigation of Doppler Frequency Shift - 48 -
3.3 Retrieval of Training Data of Vessels - 53 -
3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 -
Chapter 4. Methodology on Object Detection Architecture - 66 -
Chapter 5. Results - 74 -
5.1 Assessment on Training Data - 74 -
5.2 Assessment on AIS-based Ship Detection - 79 -
5.3 Assessment on VPASS-based Fishing Boat Detection - 91 -
Chapter 6. Discussions - 110 -
6.1 Discussion on AIS-Based Ship Detection - 110 -
6.2 Application on Determining Unclassified Vessels - 116 -
Chapter 7. Conclusion - 125 -
κ΅λ¬Έ μμ½λ¬Έ - 128 -
Bibliography - 130 -Maste
Advances in Object and Activity Detection in Remote Sensing Imagery
The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms
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
Agulhas current variability determined from space : a multi-sensor approach
Includes bibliographical references (p. 119-132).Satellite remote sensing datasets including more than 6 years of high frequency Sea Surface Temperature (SST) imagery as well as surface current observations derived from 18 years of merged-altimetry and over 2 years of Advanced Synthetic Aperture Radar (ASAR) observations are combined to study the variability of the Agulhas Current. The newly available rangedirected surface currents velocities from ASAR, which rely on the careful analysis of the measured Doppler shift, show strong promise for monitoring the meso to sub-mesoscale features of the surface circulation. While the accuracy of ASAR surface current velocities suffers from occasional bias due to our current inability to systematically account for the wind-induced contribution to the Doppler shift signal, the ASAR surface current velocities are able to consistently highlight regions of strong current and shear. The synaptic nature and relatively high resolution of ASAR acquisitions make the ASAR derived current velocities a good complement to altimetry for the study of sub-mesoscale processes and western boundary current dynamics. Time-averaged range-directed surface currents derived from ASAR provide an improved map of the mean Agulhas Current flow, clearly showing the location of the Agulhas Current core over the 1000 m isobath and identifying the region at the shelf edge of the north-eastern Agulhas Bank as one of the most variable within the Agulhas Current. To determine the variability of the Agulhas Current, an algorithm to track the position of the current is developed and applied to the longer merged-altimetry and SST records. Limitations associated with altimetry near the coast favour the use of the SST dataset to track the position of the Agulhas Current in its northern region. In the southern Agulhas, where the current lies further from the coast, altimetry is suited to monitoring the position of the Agulhas Current. The front detection analysis conducted on the SST dataset in the northern Agulhas reveals the complex nature of Natal Pulses. The downstream passage of the Natal Pulses is associated with the generation of secondary offshore meanders at the inshore edge of the current. Perturbations formed during the passage of Natal Pulses evolve rapidly to either dissipate, re-merge with the initial Natal Pulse or in some rare occasion, detach from the Agulhas Current
- β¦