A New Pan-Sharpening Method Using a Compressed Sensing Technique

By Shutao Li, Bin Yang. IEEE Transactions on Geoscience and Remote Sensing, 2011

444 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/95fffc9236ae3677bbc38d3ec9a2cec8d3c51509

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening (2021, background): “PAN and MS images are quite common in the ﬁeld of remote sensing imaging, and they are usually simultaneously acquired by sensors mounted on many satellites, such as IKONOS, variational approaches [24]–[36], and compressed sensing techniques [37]–[39].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “For each category, from left to right, the circles correspond to [42]–[50] for CS; [51]–[57] for MRA; [27], [31], and [58]–[63] for geostatistical; [64]–[69] for subspace; and [70]–[72] for sparse.”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, unknown): “[92] S.”
Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries (2013, methodology): “Compared with the method presented in [16], our method has the following novelties.”
A Sparse Image Fusion Algorithm With Application to Pan-Sharpening (2013, methodology): “A first successful attempt is addressed in [7] where multispectral image patches are assumed to have a sparse representation in a dictionary randomly sampled from HR multispectral images acquired by “comparable” sensors.”
Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, methodology): “Works have been done to develop pan-sharpening method by employing sparse representation techniques [15, 16].”
Deep Hyperspectral Image Sharpening (2018, unknown): “Pan-sharpening methods mainly include component substitution methods [31], variational methods [32], [33], and deep learning-based methods [34], and so on.”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “based methods, for example, have been successfully applied to pansharpening in several papers [31]–[36].”
Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, methodology): “Representative methods in pan-sharpening include the Intensity-Hue-Saturation (IHS) transform [22], PCA-based method [23], and compressed sensing based method [16].”
A New Pansharpening Algorithm Based on Total Variation (2014, methodology): “The approach we take here is to apply the model to the whole dataset instead of dividing the data into small patches and applying the model to these separately, as was done in [2].”
Pansharpening by Convolutional Neural Networks (2016, methodology): “In [29] a method based on compressive sensing with sparsity-inducing prior information has been proposed, where sparsity is enforced by building a dictionary of image patches randomly sampled from high-resolution MS images.”
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, methodology): “Representative pan-sharpening approaches include the sparse representation based methods [10], [11], the variational methods [12], [13], and the morphological methods [14], etc.”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, background): “Another class of methods belonging to the VO family relies on sparse representations [81].”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, background): “A large number of pan-sharpening techniques have been developed over the past two decades; representative techniques can be roughly categorized as follows: 1) component substitution (CS) [19]–[21], 2) multiresolution analysis (MRA) [22], [23], and 3) sparse representation (SR) [18], [24], [25].”
A New Pan-Sharpening Method With Deep Neural Networks (2015, methodology): “Li and Yang [8] first proposed a CS-based pan-sharpening method, which has achieved a great success.”
A Critical Comparison Among Pansharpening Algorithms (2015, background): “Among the latter, it is possible to refer to the seminal works [29], [30] that introduced this approach.”