Synthetic Aperture Radar Ship Detection Using Haar-Like Features

Schwegmann, C. P.; Kleynhans, W.; Salmon, B.P.

doi:10.1109/lgrs.2016.2631638

Cited by 63 publications

(29 citation statements)

References 10 publications

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“…An Airbus A380 airliner is used as the target, which is shown in Figure 14. The SAR signal space is calculated by using Equation (18) and processed by the modified range-doppler algorithm to produce the final images [25].…”

Section: Performance Analysismentioning

confidence: 99%

“…The subsequent processed radar images can be used for Earth remote sensing and target detection. Such a system could detect ships, whose automatic identification system (AIS) is turned off, or airliners without a beacon signal when combined with information from other sources [18]. This system can be used for future maritime and aviation security applications, for quick response in disaster monitoring and space debris detection [19].…”

mentioning

confidence: 99%

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Solar Radiation Pressure Enabled Femtosatellite-Based Earth Remote Sensing

Cao

Clemente

McInnes

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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Recent developments in electronics have pushed miniaturised satellites to the femto-scale, with masses between 10 and 100 g. Although femtosatellites have been proven as a feasible concept, most designs are limited in mission capacity and lifetime due to the lack of environmental protection and onboard propellant. In this paper, a novel concept for femtosatellites for Earth remote sensing is proposed. In particular, a swarm of femtosatellites are used as elements of a sparse array in orbit to receive radar echoes. They also feature active orbit control enabled by solar radiation pressure to extend their lifetime. A simple active orbit control algorithm has been demonstrated. A mission concept based on a Sun-synchronous circular orbit is proposed to maximise the benefit for both Earth remote sensing and active orbit control. A synthetic aperture radar mission has been used to characterise their performance.

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Section: Performance Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Solar Radiation Pressure Enabled Femtosatellite-Based Earth Remote Sensing

Cao

Clemente

McInnes

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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“…Schwegmann et al [21] used this kind of images and applied a low-threshold constant false alarm rate (CFAR) to identify ship candidates, after which they proposed a Haarlike feature extraction. The features obtained in this step were then fed into an adaptable cascade classifier.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

Nieto-Hidalgo

Gallego

Gil

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper presents a system for the detection of ships and oil spills using side-looking airborne radar (SLAR) images. The proposed method employs a two-stage architecture composed of three pairs of convolutional neural networks (CNNs). Each pair of networks is trained to recognize a single class (ship, oil spill, and coast) by following two steps: a first network performs a coarse detection, and then, a second specialized CNN obtains the precise localization of the pixels belonging to each class. After classification, a postprocessing stage is performed by applying a morphological opening filter in order to eliminate small look-alikes, and removing those oil spills and ships that are surrounded by a minimum amount of coast. Data augmentation is performed to increase the number of samples, owing to the difficulty involved in obtaining a sufficient number of correctly labeled SLAR images. The proposed method is evaluated and compared to a single multiclass CNN architecture and to previous state-of-the-art methods using accuracy, precision, recall, F-measure, and intersection over union. The results show that the proposed method is efficient and competitive, and outperforms the approaches previously used for this task.

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“…For decades, major achievements in this area focus on synthetic aperture radar (SAR) images [1][2][3], which are less influenced by time and weather. However, the resolutions of SAR images are low and the targets lack texture and color features.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

Liu

Dong

et al. 2017

Remote Sensing

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Abstract:Ship detection by Unmanned Airborne Vehicles (UAVs) and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets are viewed as uncommon regions in the sea background caused by the differences in colors, textures, shapes, or other factors. Inspired by this fact, a global saliency model is constructed based on high-frequency coefficients of the multi-scale and multi-direction wavelet decomposition, which can characterize different feature information from edge to texture of the input image. To further reduce the false alarms, a new and effective multi-level discrimination method is designed based on the improved entropy and pixel distribution, which is robust against the interferences introduced by islands, coastlines, clouds, and shadows. The experimental results on optical remote sensing images validate that the presented saliency model outperforms the comparative models in terms of the area under the receiver operating characteristic curves core and the accuracy in the images with different sizes. After the target identification, the locations and the number of the ships in various sizes and colors can be detected accurately and fast with high robustness.

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Synthetic Aperture Radar Ship Detection Using Haar-Like Features

Cited by 63 publications

References 10 publications

Solar Radiation Pressure Enabled Femtosatellite-Based Earth Remote Sensing

Solar Radiation Pressure Enabled Femtosatellite-Based Earth Remote Sensing

Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

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