Ship classification for unbalanced SAR dataset based on convolutional neural network

Li, Jianwei; Qu, Changsheng; Peng, Shujuan

doi:10.1117/1.jrs.12.035010

Cited by 43 publications

(26 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of deep learning, features of the ship are extracted automatically by CNN without manual involvement. There are fewer reports visualizing the feature maps [10][11][12][13][14][15][16][17][18][19][20] because the detection system based on deep learning is a black-box model. In other words, the feature extracted by the convolution neural network is abstract and difficult to explain [8].…”

Section: Feature Mapsmentioning

confidence: 99%

High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

Zhang

2019

Remote Sensing

168

View full text Add to dashboard Cite

As an active microwave sensor, synthetic aperture radar (SAR) has the characteristic of all-day and all-weather earth observation, which has become one of the most important means for high-resolution earth observation and global resource management. Ship detection in SAR images is also playing an increasingly important role in ocean observation and disaster relief. Nowadays, both traditional feature extraction methods and deep learning (DL) methods almost focus on improving ship detection accuracy, and the detection speed is neglected. However, the speed of SAR ship detection is extraordinarily significant, especially in real-time maritime rescue and emergency military decision-making. In order to solve this problem, this paper proposes a novel approach for high-speed ship detection in SAR images based on a grid convolutional neural network (G-CNN). This method improves the detection speed by meshing the input image, inspired by the basic thought of you only look once (YOLO), and using depthwise separable convolution. G-CNN is a brand new network structure proposed by us and it is mainly composed of a backbone convolutional neural network (B-CNN) and a detection convolutional neural network (D-CNN). First, SAR images to be detected are divided into grid cells and each grid cell is responsible for detection of specific ships. Then, the whole image is input into B-CNN to extract features. Finally, ship detection is completed in D-CNN under three scales. We experimented on an open SAR Ship Detection Dataset (SSDD) used by many other scholars and then validated the migration ability of G-CNN on two SAR images from RadarSat-1 and Gaofen-3. The experimental results show that the detection speed of our proposed method is faster than the existing other methods, such as faster-regions convolutional neural network (Faster R-CNN), single shot multi-box detector (SSD), and YOLO, under the same hardware environment with NVIDIA GTX1080 graphics processing unit (GPU) and the detection accuracy is kept within an acceptable range. Our proposed G-CNN ship detection system has great application values in real-time maritime disaster rescue and emergency military strategy formulation.

show abstract

Section: Feature Mapsmentioning

confidence: 99%

High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

Zhang

2019

Remote Sensing

168

View full text Add to dashboard Cite

show abstract

“…They evaluated the performance of this method with NWPU VHR-10 object detection dataset, which includes aircraft, ships, bridges and so on, and achieved a relatively high detection accuracy. Li, Qu, and Peng (2018) designed a loss function that the intraclass compactness and interclass separability are maximized simultaneously for ship detection in SAR images. They designed a CNN model of dense residual network based on ResNet50 (He et al, 2016), and used OpenSARShip (Huang, Liu, et al, 2017) dataset to evaluate ship classification accuracy.…”

Section: Loss Functions Of Cnnsmentioning

confidence: 99%

A survey of remote sensing image classification based on CNNs

et al. 2019

View full text Add to dashboard Cite

With the development of earth observation technologies, the acquired remote sensing images are increasing dramatically, and a new era of big data in remote sensing is coming. How to effectively mine these massive volumes of remote sensing data are new challenges. Deep learning provides a new approach for analyzing these remote sensing data. As one of the deep learning models, convolutional neural networks (CNNs) can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data. CNNs have achieved remarkable success in computer vision. In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification. We first briefly introduce the principles and characteristics of CNNs. We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification, available datasets for remote sensing image classification, and data augmentation techniques. Then, three typical CNN application cases in remote sensing image classification: scene classification, object detection and object segmentation are presented. We also discuss the problems and challenges of CNN-based remote sensing image classification, and propose corresponding measures and suggestions. We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.

show abstract

“…OpenSARship [41] has 10 categories. But the samples are extremely imbalanced between the categories, and it's hard to train the high-performance classification model with this dataset [42]. Ship chips are designed as small size image for ship classification.…”

Section: Introductionmentioning

confidence: 99%

HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

et al. 2020

View full text Add to dashboard Cite

With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation. Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective shortcomings, such as the relatively small size of SAR images for ship detection, limited SAR training samples, and inappropriate annotations, in existing SAR ship datasets, related research is hampered. To promote the development of CNN based ship detection and instance segmentation, we have constructed a High-Resolution SAR Images Dataset (HRSID). In addition to object detection, instance segmentation can also be implemented on HRSID. As for dataset construction, under the overlapped ratio of 25%, 136 panoramic SAR imageries with ranging resolution from 1m to 5m are cropped to 800 x 800 pixels SAR images. To reduce wrong annotation and missing annotation, optical remote sensing imageries are applied to reduce the interferes from harbor constructions. There are 5604 cropped SAR images and 16951 ships in HRSID, and we have divided HRSID into a training set (65% SAR images) and test set (35% SAR images) with the format of Microsoft Common Objects in Context (MS COCO). 8 state-of-the-art detectors are experimented on HRSID to build the baseline; MS COCO evaluation metrics are applicated for comprehensive evaluation. Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID.

show abstract

Ship classification for unbalanced SAR dataset based on convolutional neural network

Cited by 43 publications

References 21 publications

High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

A survey of remote sensing image classification based on CNNs

HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

Contact Info

Product

Resources

About