Multiobjective Sparse Subpixel Mapping for Remote Sensing Imagery

Song, Mansuk; Zhong, Yanfei; Ma, Ailong; Feng, Ruyi

doi:10.1109/tgrs.2019.2891354

Cited by 17 publications

(7 citation statements)

References 61 publications

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“…In formula (12): L λ is the radiance of the remote sensing λ-spectrum segment received by the detector load; E sλ is the solar irradiance reaching the outer layer of the atmosphere, which is related to the azimuth of the sun; σ is the solar zenith angle; r(λ) is the spectral reflectance of ground objects in λ band; r d (λ) is the diffuse reflectance of ground objects; τ 1 (λ) is the atmospheric transmittance of the sun to the ground, τ 2 (λ) is the atmospheric transmittance of the ground to the detector load; F is the sky shape parameter with the range of 0 ∼ 1; ε(λ) is the specific spectral emissivity of the ground feature; L T λ is the spectral heat radiation brightness of the ground feature at temperature T ; E dsλ is the irradiance of solar radiation scattered by the atmosphere and reflected by the ground; E dελ is the radiation illuminance of the atmospheric downward thermal radiation reflected by the ground; L bsλ is the radiation brightness of the sky background light reflected from the ground; L bελ is the background thermal radiation brightness reflected from the ground; L usλ is the radiation brightness of the sunlight scattered by the atmosphere; L uελ is the upward thermal radiation brightness of the atmosphere.…”

Section: Super-resolution Reconstruction Of High-resolution Images In...mentioning

confidence: 99%

“…In 2019, Gao et al [11] proposed to reduce the computational complexity through several multi-scale deep neural networks, but this method relies too much on the design of the network and has higher requirements on the scale design of the network. In 2019, Song et al [12] proposed a new remote sensing image multi-objective SSM (MOSSM) framework, which transformed the SSM problem into a multi-objective optimization problem. However, this method is ended at the pixel classification, and the ground feature recognition and radiation transfer calculation are not carried out for the pixel classification image without over division, which is not a complete image super score method.…”

Section: Introductionmentioning

confidence: 99%

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Super-resolution Inversion and Reconstruction for Remote Sensing Images of Bands of Interest

Yan¹,

Chen²,

Zhang³

et al. 2022

jist

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The super-resolution inversion and reconstruction algorithm of remote sensing image of band of interest is proposed in this paper. Based on the existing multi-resolution hyperspectral images, a spectral reflectance dictionary is constructed, according to which the objects in radiation calibrated and atmospheric corrected scene spectral reflectance images are sorted and classified using the end element extraction algorithm and the correlation distance method. Based on the classification results, a dictionary of scene feature spectral reflectance is constructed. Based on this, the sparse representation method and the secondary optimization algorithm based on image similarity are used to solve and optimize the feature distribution of the scene image. According to the feature distribution of the scene image, using the remote sensing link imaging model, super-resolution inversion reconstruction is performed to obtain a high-resolution remote sensing image of a band of interest. Experimental results show that the proposed algorithm can reconstruct low-resolution remote-sensing images of different bands into high-resolution remote-sensing images. Thus, the proposed algorithm can effectively boost image resolution, enrich image details and improve the target detection capability by the image.

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Section: Super-resolution Reconstruction Of High-resolution Images In...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Super-resolution Inversion and Reconstruction for Remote Sensing Images of Bands of Interest

Yan¹,

Chen²,

Zhang³

et al. 2022

jist

View full text Add to dashboard Cite

show abstract

“…The aim of the proposed STI is to minimize T. In STI, we consider a weighted sum of the space and temperature elements of STI, because this information-fusion method has a simple physical meaning and is easy to implement. Of course, we can also use other more effective information-fusion techniques, such as multiobjective optimization [44], alpha integration [45], and so on.…”

Section: Implementation Of Stimentioning

confidence: 99%

Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

Wang

Zhang

et al. 2019

Remote Sensing

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Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

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“…He et al [11] proposed a convolutional neural network framework for subpixel location. Song et al [12] proposed a new SSM framework for hyper-spectral images, using the theory of multi-objective optimization to solve the sub-pixel positioning problem.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Information Extraction and Application Based on Improved Pixel Exchange Algorithm

Luo¹,

Qiu-hua²,

Deng³

et al. 2022

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High resolution images can better reflect the size, shape and structural characteristics of ground objects, but due to factors such as purchase cost and observation period, it is often difficult to meet the practical application needs. Sub-pixel mapping technology can effectively improve the resolution of the results, which solves the above problem to some extent. The relevant algorithms of sub-pixel mapping include mixed pixel decomposition, end pixel extraction, sub-pixel positioning and other sub fields. The most classical sub-pixel positioning methods often use spatial correlation to define the positioning criteria. In this paper, NDWI and OTSU segmentation are used to constrain sub-pixel decomposition to some extent, which improves the performance of PSA algorithm, so as to more accurately extract river shoreline information, improve the resolution of monitoring on shoreline erosion collapse and dynamic change in the bank collapse area, and enhance the applicability of satellite remote sensing in the bank collapse monitoring field.

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Multiobjective Sparse Subpixel Mapping for Remote Sensing Imagery

Cited by 17 publications

References 61 publications

Super-resolution Inversion and Reconstruction for Remote Sensing Images of Bands of Interest

Super-resolution Inversion and Reconstruction for Remote Sensing Images of Bands of Interest

Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

Remote Sensing Image Information Extraction and Application Based on Improved Pixel Exchange Algorithm

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