Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries

By Shutao Li, Haitao Yin, Leyuan Fang. IEEE Transactions on Geoscience and Remote Sensing, 2013

296 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/24372f89f642eb92a1dbaaa0d7a5ef82ae774478

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Pansharpening by Convolutional Neural Networks (2016, background): “Further developments have been proposed in [30–32] with the goal to avoid the cost of dictionary construction.”
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation (2014, background): “Motived by the sparse coding mechanism of human vision systems [24], sparse representation [25] has been demonstrated to be an extremely powerful tool in many signal processing applications, such as denoising [26], [27], interpolation [28], and fusion [29].”
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification (2015, background): “learned dictionaries for image pansharpening [34], saliencybased codes for segmentation [35], [36], sparse bag-of-words”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “In particular, algorithms for learning dictionaries have been applied recently to the problem of image pansharpening following restoration-based approaches [90]– [93].”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “The main challenge is the generation of the dictionary, with contributions ranging from using available PAN and LRMS images [82] to using the data at hand [83]–[87] to create the dictionary.”
A New Pan-Sharpening Method With Deep Neural Networks (2015, methodology): “[10] proposed a pan-sharpening method over learned dictionary without a training set.”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “Compressive sensing and dictionary based methods, for example, have been successfully applied to pansharpening in several papers [31], [32], [33], [34], [35], [36].”
Pixel-level image fusion: A survey of the state of the art (2017, methodology): “…activity level measurement[42, 46], choose-max [42, 46, 51] and weighted average [54] based coeﬃcients combining, substitution of sparse coeﬃcients [52, 56], spatial context based weighted average[57] Methods in other domains spatial domain (non transforms) [58–73], intensity-hue-saturation…”