An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images

Shen, Huanfeng; Meng, Xiangchao; Zhang, Liangpei

doi:10.1109/tgrs.2016.2596290

Cited by 286 publications

(129 citation statements)

References 77 publications

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“…The integration of spatial, spectral and temporal resolution [6] using hidden markov model, PSM/MS model and filtering methods are used to obtain high spatial-temporal -spectral resolution fused data. The main advantage is low noise distortion in the obtained fused image but its efficiency is low.…”

Section: Temporal Resolutionmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth's surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.

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Section: Temporal Resolutionmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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“…Traditional pan-sharpening algorithms can be divided into three major branches: Component Substitution (CS) [1][2], Detail Injection (DI) [3] [4], and regularization constraint model based methods [5] [6]. In the former two branches, the fusing process is usually split into discrete steps, instead of end-to-end mapping.…”

Section: Introductionmentioning

confidence: 99%

“…Four metrics: Q, ERGAS, SAM, and SCC are employed to quantify its accuracy in spatial and spectral domains, with the original MS image as ground truth. The performances of DRPNN are compared with five algorithms from different branches for comparisons with the proposed network, including Component Substitution: GS [1], Detail Injection: MTF-GLP [3], SFIM [4], regularization constraint models: ISTS [5] based on total variation and TSSC [6] based on sparse representation. Besides these traditional algorithms, PNN [10], a shallow CNN without recidual learning and skip connection has also been included for comparison.…”

Section: Introductionmentioning

confidence: 99%

Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network

Wei

Yuan

Shen

et al. 2017

IEEE Geosci. Remote Sensing Lett.

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424

198

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Abstract-In the field of multi-spectral and panchromatic images fusion (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy. However, to the best of our knowledge, existing research works are mainly based on simple and flat networks with relatively shallow architecture, which severely limited their performances. In this paper, the concept of residual learning has been introduced to form a very deep convolutional neural network to make a full use of the high non-linearity of deep learning models. By both quantitative and visual assessments on a large number of high quality multi-spectral images from various sources, it has been supported that our proposed model is superior to all mainstream algorithms included in the comparison, and achieved the highest spatial-spectral unified accuracy.

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“…The model-based methods regard the fusion process as an ill-posed inverse optimization problem, and construct the energy functional based on the HR-PAN image, the LR-M S images and the ideal fused image. Then iterative optimization algorithm, such as the gradient des cent algorithm [6], the conjugate gradient algorithm [7], the split Bregman iteration algorithm [8], and the alternating direction method of multipliers (ADMM) algorithm [9], is used to solve the model to get the fused image.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Fusion by Combining Deep Learning and Variational Model

Shen

Jiang

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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in the field of spatial-spectral fusion, the modelbased method and the deep learning (DL)-based method are state-of-the-art. This paper presents a fusion method that incorporates the deep neural network into the model -based method for the most common case in the spatial-spectral fusion: PAN/multispectral (MS ) fusion. S pecifically, we first map the gradient of the high spatial resolution panchromatic image (HR-PAN) and the low spatial resolution multispectral image (LR-MS ) to the gradient of the high spatial resolution multispectral image (HR-MS ) via a deep residual convolutional neural network (CNN). Then we construct a fusion framework by the LR-MS image, the gradient prior learned from the gradient network, and the ideal fused image. Finally, an iterative optimization algorithm is used to solve the fusion model. Both quantitative and visual assessments on high-quality images from various sources demonstrate that the proposed fusion method is superior to all the mainstream algorithms included in the comparison in terms of overall fusion accuracy.

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An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images

Cited by 286 publications

References 77 publications

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network

Spatial–Spectral Fusion by Combining Deep Learning and Variational Model

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