Color constancy enhancement for multi-spectral remote sensing images

Mi, Wang; Zheng, Xinghui; Feng, Chunhui

doi:10.1109/igarss.2013.6721296

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…By referring to the relevant works of multispectral remote sensing image enhancement [30][31][32][33][34][35][36][37][38], five well-known evaluation indexes, including the contrast, image intensity, information entropy, average gradient, and execution time are used to evaluate the performance of different methods.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In [33], Shilpa Suresh et al exploited a novel framework for the enhancement of multispectral images, which primarily aimed to highlight the contrast of color-synthesis remote sensing images through a modified linking synaptic computation network (MLSCN). Wang et al [34] exploited a color constancy algorithm, which used the improved linear transformation function to improve the brightness while avoiding color distortion. Shan-long Lu et al [35] introduced a multispectral satellite remote sensing image enhancement algorithm based on the combination of PCA and the intensity-hue-saturation (IHS) transform.…”

Section: Related Workmentioning

confidence: 99%

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Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

et al. 2022

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Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

et al. 2022

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“…Users can optimize it with other image enhancement methods. [21][22][23][24] Experiments and results…”

Section: Image Visualizationmentioning

confidence: 99%

Improved interactive color visualization approach for hyperspectral images

2021

Information Visualization

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Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

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“…Additionally, other studies involving hyperspectral images for remote sensing in the field of mineral exploration, precision agriculture, forest research, earth science research, and environmental monitoring, where it offers the advantage of achieving a spectral resolution at the nanoscale, and thereby obtaining a volume of very narrow and contiguous images for further analysis of comparison [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Tunable Optical Spectral Imaging System Using a Grating in the Pupil Function

Garcia-Diaz¹,

Palillero-Sandoval²,

Escobedo-Alatorre³

et al. 2022

IEEE Access

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Hyperspectral imaging (HSI) systems have been demonstrated as a powerful imaging technique due to their high spectral resolution. HSI can obtain the spectrum for each pixel in the image of a scene, a feature that can be exploited to design optical systems with the purpose of analyzing and characterizing objects and identifying processes within the visible electromagnetic spectrum (bandwidth). In this paper, we present an HSI system comprising a diffraction grating placed in the exit pupil of our optical configuration. The spectrum for each pixel associated with the object appears in the first order of diffraction. We used this system to characterize and tune the required spectral band of the image of the captured object obtaining more information than with an optical imaging system. Accordingly, the proposed optical system is suitable to obtain spectral and hyperspectral imaging at low cost compared to an acoustooptic system or other HSI. The scanning system captures hundreds of spectral images associated with the object, obtaining a maximum spectral resolution of 0.26nm or 260 pm for one of our configurations.INDEX TERMS Hyperspectral imaging, spectral images, diffraction grating.

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Color constancy enhancement for multi-spectral remote sensing images

Cited by 7 publications

References 5 publications

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Improved interactive color visualization approach for hyperspectral images

Experimental Analysis of Tunable Optical Spectral Imaging System Using a Grating in the Pupil Function

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