SRSDD-v1.0: A High-Resolution SAR Rotation Ship Detection Dataset

Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. The dataset contains six categories of ships. In total, 30 panoramic SAR tiles of the Chinese Gaofen-3 of port areas with a 1-m resolution were cropped to slices, each with 1024 × 1024 pixels. In addition, most of the images in the dataset contain nearshore areas with complex background interference. Eight state-of-the-art rotated detectors and a CFAR-based method were used to evaluate the dataset. Experimental results revealed that the complex background will have a great impact on the performance of detectors.

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Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images

Remote Sensing ◽

10.3390/rs12162619 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2619 ◽

Cited By ~ 2

Author(s):

Fei Gao ◽

Yishan He ◽

Jun Wang ◽

Amir Hussain ◽

Huiyu Zhou

Keyword(s):

Deep Learning ◽

High Resolution ◽

Sar Images ◽

Convolutional Network ◽

Feature Map ◽

Ship Detection ◽

Detection Algorithms ◽

Feature Aggregation ◽

Small Targets ◽

Detection Speed

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learning-based algorithms bring promising performance due to end-to-end detection and automated feature extraction. However, several challenges still exist: (1) standard deep learning detectors based on anchors have certain unsolved problems, such as tuning of anchor-related parameters, scale-variation and high computational costs. (2) SAR data is huge but the labeled data is relatively small, which may lead to overfitting in training. (3) To improve detection speed, deep learning detectors generally detect targets based on low-resolution features, which may cause missed detections for small targets. In order to address the above problems, an anchor-free convolutional network with dense attention feature aggregation is proposed in this paper. Firstly, we use a lightweight feature extractor to extract multiscale ship features. The inverted residual blocks with depth-wise separable convolution reduce the network parameters and improve the detection speed. Secondly, a novel feature aggregation scheme called dense attention feature aggregation (DAFA) is proposed to obtain a high-resolution feature map with multiscale information. By combining the multiscale features through dense connections and iterative fusions, DAFA improves the generalization performance of the network. In addition, an attention block, namely spatial and channel squeeze and excitation (SCSE) block is embedded in the upsampling process of DAFA to enhance the salient features of the target and suppress the background clutters. Third, an anchor-free detector, which is a center-point-based ship predictor (CSP), is adopted in this paper. CSP regresses the ship centers and ship sizes simultaneously on the high-resolution feature map to implement anchor-free and nonmaximum suppression (NMS)-free ship detection. The experiments on the AirSARShip-1.0 dataset demonstrate the effectiveness of our method. The results show that the proposed method outperforms several mainstream detection algorithms in both accuracy and speed.

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Accelerating Super-Resolution and Visual Task Analysis in Medical Images

Applied Sciences ◽

10.3390/app10124282 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4282

Author(s):

Ghada Zamzmi ◽

Sivaramakrishnan Rajaraman ◽

Sameer Antani

Keyword(s):

Deep Learning ◽

High Resolution ◽

Task Analysis ◽

Multiple Scales ◽

Medical Images ◽

Computational Cost ◽

Super Resolution ◽

Visual Task ◽

Learning Networks ◽

High Resolution Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

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Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.8.577 ◽

2021 ◽

Vol 87 (8) ◽

pp. 577-591

Author(s):

Fengpeng Li ◽

Jiabao Li ◽

Wei Han ◽

Ruyi Feng ◽

Lizhe Wang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Network ◽

State Of The Art ◽

Remote Sensing Image ◽

Scene Classification ◽

Data Set ◽

Unsupervised Deep Learning

Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set.

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Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Sensors ◽

10.3390/s21041033 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1033

Author(s):

Qiaodi Wen ◽

Ziqi Luo ◽

Ruitao Chen ◽

Yifan Yang ◽

Guofa Li

Keyword(s):

Neural Network ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Network ◽

Target Detection ◽

Defect Detection ◽

Region Of Interest ◽

Aerial Images ◽

Detection Methods ◽

Complex Background

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

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DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019607 ◽

2019 ◽

Vol 33 ◽

pp. 9607-9612 ◽

Cited By ~ 8

Author(s):

Lijing Wang ◽

Jiangzhuo Chen ◽

Madhav Marathe

Keyword(s):

Deep Learning ◽

High Resolution ◽

State Of The Art ◽

Synthetic Data ◽

State Level ◽

The Other ◽

County Level ◽

Short Term ◽

Neural Network Structure ◽

Epidemic Forecasting

Influenza-like illness (ILI) is among the most common diseases worldwide. Producing timely, well-informed, and reliable forecasts for ILI is crucial for preparedness and optimal interventions. In this work, we focus on short-term but highresolution forecasting and propose DEFSI (Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods. In DEFSI, we build a two-branch neural network structure to take both within-season observations and between-season observations as features. The model is trained on geographically highresolution synthetic data. It enables detailed forecasting when high-resolution surveillance data is not available. Furthermore, the model is provided with better generalizability and physical consistency. Our method achieves comparable/better performance than state-of-the-art methods for short-term ILI forecasting at the state level. For high-resolution forecasting at the county level, DEFSI significantly outperforms the other methods.

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Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

Remote Sensing ◽

10.3390/rs13132482 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2482

Author(s):

Pedro Zamboni ◽

José Marcato Junior ◽

Jonathan de Andrade Silva ◽

Gabriela Takahashi Miyoshi ◽

Edson Takashi Matsubara ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

State Of The Art ◽

Free State ◽

Two Stage ◽

Learning Methods ◽

Single Tree ◽

Sensing Applications ◽

Remote Sensing Applications

Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.

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A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

Remote Sensing ◽

10.3390/rs11070765 ◽

2019 ◽

Vol 11 (7) ◽

pp. 765 ◽

Cited By ~ 26

Author(s):

Yuanyuan Wang ◽

Chao Wang ◽

Hong Zhang ◽

Yingbo Dong ◽

Sisi Wei

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Natural Images ◽

Average Precision ◽

Sar Images ◽

Test Dataset ◽

Ship Detection ◽

Art Object ◽

Public Dataset ◽

Sar Imagery

With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning achieve top performance, benefitting from a free public dataset. Unfortunately, due to the lack of a large volume of labeled datasets, object detectors for SAR ship detection have developed slowly. To boost the development of object detectors in SAR images, a SAR dataset is constructed. This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. Moreover, modified state-of-the-art object detectors from natural images are trained and can be used as baselines. Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset we constructed.

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1D Barcode Detection via Integrated Deep-Learning and Geometric Approach

Applied Sciences ◽

10.3390/app9163268 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3268 ◽

Cited By ~ 3

Author(s):

Yunzhe Xiao ◽

Zhong Ming

Keyword(s):

Deep Learning ◽

High Resolution ◽

State Of The Art ◽

Computational Cost ◽

Geometric Approach ◽

Integrated Approach ◽

High Demand ◽

Image Region ◽

High Resolution Images ◽

The Subject

Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.

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Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images

Entropy ◽

10.3390/e21030221 ◽

2019 ◽

Vol 21 (3) ◽

pp. 221 ◽

Cited By ~ 1

Author(s):

Ido Cohen ◽

Eli David ◽

Nathan Netanyahu

Keyword(s):

Image Processing ◽

Gene Ontology ◽

Deep Learning ◽

In Situ Hybridization ◽

High Resolution ◽

State Of The Art ◽

Processing Methods ◽

Previous State ◽

Traditional Image

In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning functional representations of genes, based on their expressions in these brain images. In this paper, we describe a novel end-to-end deep learning-based method for generating compact representations of in situ hybridization (ISH) images, which are invariant-to-translation. In contrast to traditional image processing methods, our method relies, instead, on deep convolutional denoising autoencoders (CDAE) for processing raw pixel inputs, and generating the desired compact image representations. We provide an in-depth description of our deep learning-based approach, and present extensive experimental results, demonstrating that representations extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Our methods improve the previous state-of-the-art classification rate (Liscovitch, et al.) from an average AUC of 0.92 to 0.997, i.e., it achieves 96% reduction in error rate. Furthermore, the representation vectors generated due to our method are more compact in comparison to previous state-of-the-art methods, allowing for a more efficient high-level representation of images. These results are obtained with significantly downsampled images in comparison to the original high-resolution ones, further underscoring the robustness of our proposed method.

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MSR2N: Multi-Stage Rotational Region Based Network for Arbitrary-Oriented Ship Detection in SAR Images

Sensors ◽

10.3390/s20082340 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2340 ◽

Cited By ~ 2

Author(s):

Zhenru Pan ◽

Rong Yang ◽

Zhimin Zhang

Keyword(s):

High Resolution ◽

Background Noise ◽

Rotation Angle ◽

State Of The Art ◽

Synthetic Aperture ◽

Sar Images ◽

Ship Detection ◽

Multi Stage ◽

Feature Pyramid ◽

Bounding Boxes

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance.

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