Cloud Detection in Satellite Images Based on Natural Scene Statistics and Gabor Features

Deng, Chenwei; Li, Zhen; Wang, Wenzheng; Wang, Shuigen; Tang, Linbo; Bovik, Alan C.

doi:10.1109/lgrs.2018.2878239

Cited by 32 publications

(20 citation statements)

References 19 publications

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“…To experiment the MOS calculation, the test images are displayed on a CRT screen of a Personal computer PC, according to 10.5cm x 10.5cm size (512x512 image resolution) [33][34][35][36][37][38]. The MOS factor varies from 0 up to 10 (2: very annoying degradation, 4: annoying, 6: a little bit annoying, 8: perceptible, but not annoying, 10: Imperceptible).…”

Section: Subjective Metric Mos Assessment Experimental Resultsmentioning

confidence: 99%

“…The subjective quality evaluation is normalized by the CCIR recommendations [33][34][35][36][37][38], originally designed for television images without taking into account the introduced degradations in the original image. We aim to evaluate the detected distortions between degraded image and its reference and adopt comparative measures tests.…”

Section: Conditions To Be Met In Subjective Quality Metricsmentioning

confidence: 99%

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A Perceptually Optimized Embedded Image Coder and Quality Assessor Based Both on Visual Tools

Bajit¹,

Najid²,

Tamtaoui³

et al. 2019

Adv. sci. technol. eng. syst. j.

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In this paper, we propose a new Perceptually Optimized Embedded Zero Tree Image Coder EVIC and its improved version MEVIC. The coder presents a new perceptual model to weight wavelet coefficients to enhance SPIHT embedded coding. The new visual coder aims to improve the visual quality obtained by the reference SPIHT coder for a given targeted bit rate. In addition, the paper presents three-evaluation approaches (Objective, Subjective and Quantitative) based all on a quality score PS given by the objective visual quality metric named MWVDP which is an optimized version of Daly Visible Difference Predictor VDP. It incorporates the human visual system HVS properties, correlates largely well with the mean opinion score known as MOS and provides an important feature in image coding quality assessment. The visual coders EVIC/MEVIC are fully dependent to the HVS properties, from which, they include various masking effects and visual models. Based on this model, the visual coders weight the original wavelet coefficients and reshape their spectrum to optimize the perceptual quality coding for a given observation distance and bit rate. The visual weighting model processes within all wavelet sub-bands: 1) the contrast sensitivity filter CSF to mask invisible frequencies, 2) the threshold elevation to correct the luminance and elevate the contrast, 3) the Just Detectable Distortions JND to quantize visually the wavelet coefficients according to their corresponding thresholds. The visual coder EVIC and its optimized version MEVIC have the same software complexity as their reference SPIHT. However, they perform qualitatively and quantitatively excellent results experimentation and features improvement either in image coding and quality assessing.

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Section: Subjective Metric Mos Assessment Experimental Resultsmentioning

confidence: 99%

Section: Conditions To Be Met In Subjective Quality Metricsmentioning

confidence: 99%

A Perceptually Optimized Embedded Image Coder and Quality Assessor Based Both on Visual Tools

Bajit¹,

Najid²,

Tamtaoui³

et al. 2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Authors of [32] designed a simple linear iterative clustering algorithm. It is capable to classify thin cloud, thick cloud, and non-cloudy categories.…”

Section: Thin Cloud/thick Cloudmentioning

confidence: 99%

Cloud detection methodologies: variants and development—a review

Mahajan

Fataniya

2019

Complex Intell. Syst.

109

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Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004-2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/ No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.

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“…We plan to assess the identified differences among a candidate and reference pictures and embrace instinctive measures. We expect the utilization of Fränti conditions [40][41] required for this assessment experimentation, as suggested in the CCIR recommendation detailed in [42][43] and summarized in Table. 4. Assuming that these conditions are regarded, we standardize the assessment range to evade extra blunders environment dependent.…”

Section: Requirements For An Abstract Quality Assessorsmentioning

confidence: 99%

A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions

Bajit¹,

Nahid²,

Tamtaoui³

et al. 2020

Adv. sci. technol. eng. syst. j.

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In the present article, we introduce a foveation-based optimized embedded and its optimized version image coders thereafter called VOEFIC/MOEFIC and its related foveation wavelet visible difference predictor FWVDP coding quality metric. It advances a visually advanced foveal weighting mask that regulates the wavelet-based image spectrum before its encoding by the SPIHT encoder. It intends to arrive at a destined compression rate with a significant quality improvement for a disposed of binary budget, witnessing separation, and a foveal locale that locates the object in the zone of concern ROI. The coder embodies a couple of masking achieves build on the human psycho-visual quality criteria. Hence, the coder administers the foveal model to weigh the source wavelets samples, reshapes its spectrum content, adapts its shape, discards or somewhat shrinks the redundant excess and finally enhances the visual quality. The foveal weighting mask is computed indoors wavelet subbands as come after. First, it administers the foveal wavelet-based filter depending on the intention point so that it removes or at least reduces the imperceptible frequencies around the zone of concern. Next, it augments the picture contrast according to wavelet JND thresholds to manage brightening and nice the contrast above the distortion just notable. Once refined, the weighted wavelet spectrum will be embedded coded using the standard SPIHT to reach a desired binary bit budget. The manuscript also advances a foveationbased objective quality evaluator that embodies a psycho-visual quality criterion identified with the visual cortex framework. This investigator furnishes a foveal score FPS having the power of detecting probable errors and measuring objectively the compression quality. Keep in mind that the foveal coder VOEFIC and its visually upgraded variant MOEFIC, have similar complexity as their reference SPIHT. In contrast, their gathered data highlight the visual coding advancement and the boost ratio purchased in its quality gain.

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Cloud Detection in Satellite Images Based on Natural Scene Statistics and Gabor Features

Cited by 32 publications

References 19 publications

A Perceptually Optimized Embedded Image Coder and Quality Assessor Based Both on Visual Tools

A Perceptually Optimized Embedded Image Coder and Quality Assessor Based Both on Visual Tools

Cloud detection methodologies: variants and development—a review

A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions

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