Fusion of Panchromatic and Multispectral Images via Coupled Sparse Non-Negative Matrix Factorization

Zhang, Kai; Wang, Min; Yang, Shuyuan; Xing, Yinghui; Qu, Ronghai

doi:10.1109/jstars.2015.2475754

Cited by 37 publications

(15 citation statements)

References 22 publications

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“…Nowadays many methods have been proposed for LR MS and PAN images fusion [5]. These fusion methods can be generally classified into three categories: (1) component substitution (CS) based methods [6][7][8][9], (2) multiresolution analysis (MRA)-based methods [10][11][12][13], (3) degradation model (DM)-based methods [14][15][16][17][18][19][20][21][22]. For the first category, some transforms are considered, such as the intensity-hue-saturation (IHS) transform [6], principal component analysis (PCA) [6], and Gram-Schmidt (GS) transform [7].…”

Section: Introductionmentioning

confidence: 99%

“…Some methods [16][17][18][19][20][21][22] based on DM are proposed by modeling the observation relationships among LR MS, PAN and HR MS images. In these methods, LR MS and PAN images are believed to obtain by down-sampling and filtering the HR MS image in spatial and spectral domains, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], the Huber-Markov model [23] is considered to depict the second order gradient prior in HR MS images for the fusion of LR MS and PAN images. Besides, the non-negativity of pixel values is also employed [18]. Recently, sparse prior has also been introduced into the degradation models.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

Zhang

Yang

2019

Remote Sensing

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Fusing the panchromatic (PAN) image and low spatial-resolution multispectral (LR MS) images is an effective technology for generating high spatial-resolution MS (HR MS) images. Some image-fusion methods inspired by neighbor embedding (NE) are proposed and produce competitive results. These methods generally adopt Euclidean distance to determinate the neighbors. However, closer Euclidean distance is not equal to greater similarity in spatial structure. In this paper, we propose a spatial weighted neighbor embedding (SWNE) approach for PAN and MS image fusion, by exploring the similar manifold structures existing in the observed LR MS images to those of HR MS images. In SWNE, the spatial neighbors of the LR patch are found first. Second, the weights of these neighbors are estimated by the alternative direction multiplier method (ADMM), in which the neighbors and their weights are determined simultaneously. Finally, the HR patches are reconstructed by the sum of HR patches corresponding to the LR patches multiplying with their weights. Due to the introduction of spatial structures in objective function, outlier patches can be eliminated effectively by ADMM. Compared with other methods based on NE, more reasonable neighbor patches and their weights are estimated simultaneously. Some experiments are conducted on datasets collected by QuickBird and Geoeye-1 satellites to validate the effectiveness of SWNE, and the results demonstrate a better performance of SWNE in spatial and spectral information preservation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

Zhang

Yang

2019

Remote Sensing

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“…The typical panchromatic band data obtained from airborne platforms have a much wider spectral range than is utilized by multispectral bands (red, green, blue) [18], and also have a higher radiometric resolution (number of bits per pixel). Moreover, panchromatic satellite images have a higher spatial resolution than multispectral images [19].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have employed wavelet-based techniques to address this concern [39][40][41]. The discernibility of targets from the background may vary at different scales, which can be problematic for object detection [19,42]. Wavelet analysis can transform signals into multiple resolutions, using an adaptive window [43], and thereby latently detect targets in cluttered backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

2017

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Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

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Construction of Sampling Two-Channel Nonseparable Wavelet Filter Bank and Its Fusion Application for Multispectral Image Pansharpening

Liu

2018

Advances in Multimedia Information Processing – PCM 2017

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Fusion of Panchromatic and Multispectral Images via Coupled Sparse Non-Negative Matrix Factorization

Cited by 37 publications

References 22 publications

Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Construction of Sampling Two-Channel Nonseparable Wavelet Filter Bank and Its Fusion Application for Multispectral Image Pansharpening

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