Detection and visualization of oil spill using thermal images

Oulefki, Adel; Trongtirakul, Thaweesak; Agaian, Sos С.; Chiracharit, Werapon

doi:10.1117/12.2557139

Cited by 6 publications

(4 citation statements)

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“…One of the shortcomings of oil spill detection from thermal infrared images is that natural objects, such as shorelines, sediments, and organic matter, may appear like oil in thermal infrared images, which may cause errors to the detection of oil objects [15,16]. In addition, the resolution of satellite-based thermal images is low, and thermal images are often noisy and blurry [101].…”

Section: Optical Datamentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Section: Optical Datamentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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“…Infrared sensors operate at wavelengths of 700 nm -1.1 mm, which is longer than visible light (400 -700 nm) [1]. Infrared imagery plays a vital role in military detection [2], surveillance, medical imaging [3,4], oil spill applications [5], and visualization [6,7]. Infrared has the advantage of low scattering and low lighting.…”

Section: Introductionmentioning

confidence: 99%

New retinex model-based infrared image enhancement

Trongtirakul¹,

Agaian

2023

Multimodal Image Exploitation and Learning 2023

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The thermal imaging system often suffers from low contrast, low-spatial resolution, and blur under heat radiation conditions. Currently, available image enhancement methods are most suitable for visible images. However, existing methods often enhance objects and noise for noisy infrared images, simultaneously producing a very poor result. This paper presents two single infrared image enhancement methods, including (i) a Bi-Logarithmic Histogram Equalization with Quasi-symmetric correction and (ii) a combined luminance and reflection decomposition and image fusion-based method. Computer simulations on benchmarking infrared Kuangxd database show that the proposed algorithm performance outperforms conventional image enhancement methods, including a cutting-edge learningbased method, in terms of subjective and objective evaluations.

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“…Images that exhibit a clear distinction between bright and dark regions, as well as different colors, provide more details about the scene. Such images are essential for various computer vision applications, including medical imaging segmentation [1][2][3], oil spill detection [4,5], surveillance [6,7], intelligent transportation [8,9], electrical inspection [10,11], and imaging visualization [12][13][14]. Several low-light image enhancement techniques have emerged, encompassing learning-free methods such as histogram-based algorithms, Retinex-based algorithms [15][16][17][18][19], and learning-based methods like neural network-based algorithms [20][21][22], as depicted in FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single Low-Light Image Enhancement by Fractional Stretching in Logarithmic Domain

Trongtirakul,

Agaian,

2023

IEEE Access

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Low-light image enhancement is a challenging task that aims to improve the visibility and quality of images captured in dark environments. However, existing methods often introduce undesirable artifacts such as color distortion, halo effects, blocking artifacts, and noise amplification. In this paper, we propose a novel method that overcomes these limitations by using logarithmic domain fractional stretching approach to estimate the reflectance component of the image based on the improved Retinex theory. Moreover, we apply a simple adaptive gamma correction algorithm on the Lab color space to adjust the brightness and saturation of the image. Our method effectively reduces the impact of non-uniform illumination and produces enhanced images with natural and realistic colors. Extensive experiments across diverse public datasets substantiate the superiority of our method. In both subjective and objective evaluations, our approach outperforms state-of-the-art methods.

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Detection and visualization of oil spill using thermal images

Cited by 6 publications

References 0 publications

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

New retinex model-based infrared image enhancement

Adaptive Single Low-Light Image Enhancement by Fractional Stretching in Logarithmic Domain

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