55 research outputs found
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images
NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA).
The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset
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
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
SSA-SiamNet:Spectral-Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection
Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral-spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral-spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods
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
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
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