A Framework for Quality Enhancement of Multispectral Remote Sensing Images

Suresh, Shilpa; Das, Devikalyan; Lal, Shyam

doi:10.1109/icoac.2017.8441181

Cited by 3 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 addition, A. K. Bhandari et al [32] presented a combination method of the discrete cosine transform (DCT) and SVD to highlight the contrast of color multispectral remote sensing images. 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.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In various felds, the application of the deep learning model is more and more extensive, and more and more people pay attention to the research of the model [10,11]. Among them, a convolutional neural network is a classical representative model of the deep learning model, which is often used for image recognition [12].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Target Recognition Based on Multimodule Image Enhancement Network

Wang

Yan

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) has been widely used in recent years, and SAR automatic target recognition (ATR) has become a research hotspot. Most of the existing SAR ATR methods focus on the network structure design and increasing data volume but omit image quality and real-time processing. We design a multimodule image enhancement network (MMIE-Net) to solve these problems, which include the target extraction module, the image processing module, and convolutional neural networks (CNNs). First, we use the target extraction module and the image processing module to enhance the quality of raw SAR images. Then we design a suitable network for SAR image recognition, which is simple, lightweight, and recognizable. The experiment was mainly carried out on the MSTAR dataset, which can be divided into two categories, Standard Operating Condition (SOC) and Extended Operating Condition (EOC). The identification accuracy, the parameter storage space, and the depth of the model are considered as the criterion. The experimental results show that, compared with other methods, the proposed method not only ensures the simple structure of the network model but also has better recognition accuracy. Additionally, our method is robust and stable to large depression angle variation as well.

show abstract

Investigating Spiking Neural Networks for Energy-Efficient On-Board AI Applications. A Case Study in Land Cover and Land Use Classification

Kucik

Meoni

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

A Framework for Quality Enhancement of Multispectral Remote Sensing Images

Cited by 3 publications

References 19 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

Synthetic Aperture Radar Target Recognition Based on Multimodule Image Enhancement Network

Investigating Spiking Neural Networks for Energy-Efficient On-Board AI Applications. A Case Study in Land Cover and Land Use Classification

Contact Info

Product

Resources

About