scholarly journals Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

2019 ◽  
Vol 11 (24) ◽  
pp. 2997 ◽  
Author(s):  
Clément Dechesne ◽  
Sébastien Lefèvre ◽  
Rodolphe Vadaine ◽  
Guillaume Hajduch ◽  
Ronan Fablet
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Large Scale ◽  
Visual Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Human Observer ◽  
Convolutional Network ◽  
Ship Detection

The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).

scholarly journals LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

2020 ◽  
Vol 12 (18) ◽  
pp. 2997 ◽  
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang ◽  
Xiao Ke ◽  
Xu Zhan ◽  
Jun Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Google Earth ◽  
Detection Methods ◽  
False Alarms ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Ship Detection ◽  
Small Ship

Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

scholarly journals Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1078 ◽  
Author(s):  
Ramona Pelich ◽  
Marco Chini ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Carlos Lopez-Martinez ◽  
...  
Keyword(s):  
Detection Rate ◽  
Large Scale ◽  
Added Value ◽  
Automatic Identification ◽  
Identification System ◽  
Dual Polarization ◽  
Ship Detection ◽  
Traffic Conditions ◽  
Intensity Images ◽  
Polarization Channel

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

scholarly journals DEEP LEARNING FOR VESSEL DETECTION AND IDENTIFICATION FROM SPACEBORNE OPTICAL IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 303-310
Author(s):  
G. Matasci ◽  
J. Plante ◽  
K. Kasa ◽  
P. Mousavi ◽  
A. Stewart ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Input Image ◽  
Automatic Identification ◽  
Identification System ◽  
Optical Remote Sensing ◽  
Detection Model ◽  
Model Based ◽  
Optical Images ◽  
Vessel Detection

Abstract. We present a deep learning-based vessel detection and (re-)identification approach from spaceborne optical images. We introduce these two components as part of a maritime surveillance from space pipeline and present experimental results on challenging real-world maritime datasets derived from WorldView imagery. First, we developed a vessel detection model based on RetinaNet achieving a performance of 0.795 F1-score on a challenging multi-scale dataset. We then collected a large-scale dataset for vessel identification by applying the detection model on 200+ optical images, detecting the vessels therein and assigning them an identity via an Automatic Identification System association framework. A vessel re-identification model based on Twin neural networks has then been trained on this dataset featuring 2500+ unique vessels with multiple repeated occurrences across different acquisitions. The model allows to naturally establish similarities between vessel images. It returns a relevant ranking of candidate vessels from a database when provided an input image for a specific vessel the user might be interested in, with top-1 and top-10 accuracies of 38.7% and 76.5%, respectively. This study demonstrates the potential offered by the latest advances in deep learning and computer vision when applied to optical remote sensing imagery in a maritime context, opening new opportunities for automated vessel monitoring and tracking capabilities from space.

Multi-task deep learning of near infrared spectra for improved grain quality trait predictions

2020 ◽  
Vol 28 (5-6) ◽  
pp. 275-286 ◽  
Author(s):  
S Assadzadeh ◽  
CK Walker ◽  
LS McDonald ◽  
P Maharjan ◽  
JF Panozzo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Near Infrared ◽  
Model Development ◽  
Quality Trait ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Convolutional Network ◽  
Improved Accuracy

A global predictive model was developed for protein, moisture, and grain type, using near infrared (NIR) spectra. The model is a deep convolutional neural network, trained on NIR spectral data captured from wheat, barley, field pea, and lentil whole grains. The deep learning model performs multi-task learning to simultaneously predict grain protein, moisture, and type, with a significant reduction in prediction errors compared to linear approaches (e.g., partial least squares regression). Moreover, it is shown that the convolutional network architecture learns much more efficiently than simple feedforward neural network architectures of the same size. Thus, in addition to improved accuracy, the presented deep network is very efficient to implement, both in terms of model development time, and the required computational resources.

scholarly journals RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

GigaScience ◽  
2019 ◽  
Vol 8 (11) ◽  
Author(s):  
Robail Yasrab ◽  
Jonathan A Atkinson ◽  
Darren M Wells ◽  
Andrew P French ◽  
Tony P Pridmore ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Plant Species ◽  
Network Architecture ◽  
Large Scale ◽  
Search Algorithm ◽  
System Architectures

Abstract Background In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. Results We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. Conclusions We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

scholarly journals Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus

2020 ◽  
Vol 6 (3) ◽  
pp. 501-504
Author(s):  
Dennis Schmidt ◽  
Andreas Rausch ◽  
Thomas Schanze
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Cell ◽  
Network Architecture ◽  
Image Sequences ◽  
Marburg Virus ◽  
Microscopic Image ◽  
Cell Structures ◽  
Fast Recognition ◽  
Subviral Particles

AbstractThe Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.

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