Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

Feng, Xiaoxiao; He, Lei; Cheng, Qimin; Long, Xiaoyi; Yuan, Yuxin

doi:10.3390/rs12061009

Cited by 35 publications

(18 citation statements)

References 34 publications

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“…Over decades, many methods [8,9] have been proposed to reconstruct the desired HR HSI by fusing HR MSIs and LR HSIs, including sparse representation-based methods [10,11], Bayesian-based methods [12,13], spectral unmixing-based methods [1,14], and tensor factorization-based methods [15,16]. Sparse representation-based, Bayesianbased, and spectral unmixing-based methods usually first learn spectral bases (or endmembers) from the LR HSI [9,10].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Chen

Zheng

2021

Remote Sensing

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Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Chen

Zheng

2021

Remote Sensing

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“…If all these conditions were the same at a different position globally, then the spectral signature would be similar. When the study area comprises complex vegetation forms/ different vegetation stages, it is difficult to analyze the vegetation when spectral variations are similar [5].…”

Section: Introductionmentioning

confidence: 99%

“…This requirement is feasible for Multispectral (MS) and Hyperspectral images. When working with high-resolution color images, vegetation detection computation can be accurate when specific distinct structures represent vegetation [5]. There are two solutions to extract and detect vegetation features accurately-(i) using classification algorithms and (ii) increasing the distinct possibilities through high-resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

“…There are two solutions to extract and detect vegetation features accurately-(i) using classification algorithms and (ii) increasing the distinct possibilities through high-resolution imagery. In the first case, classification algorithms require robust supervised/ unsupervised classification techniques with many trained data sets in supervised algorithms and strong inventory measures to define the classification labels, which is a time-consuming process [5]. There is a requirement of ancillary data, including field samples, topographical characteristics, environmental characteristics, and other digital layers of data (geographic information system) for enhancing the accuracy of classification.…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of Super-Resolution Technique on Vegetation Indices

Rohith

Kumar

2021

IEEE Access

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The identification and interpretation of remote sensed (RS) objects in an image depend on how well the sensor captures the region. In rare cases, RS images may be vulnerable to a lack of interpretability issues in some parts of the image due to the sensor's limits and preprocessing techniques. Conventionally, the interpretation of the land cover pattern's shape and size is apparent when the distance between the sensor and object is closer to the visualization level of objects and viable with digital airborne imagery. In this paper, integration of the Super-Resolution (SR) technique in the high-resolution imagery to achieve the closer visualization level for mapping the vegetation is proposed. This approach enables the higher interpretive potential to define the land pattern's shape and size with very high spatial resolution, closer proximity, detailed and distinguishable patterns. This approach helps to precisely predict the total vegetated study area for land use and land cover changes (LULCC) and chlorophyll-rich vegetation applications. The proposed algorithm is carried out in two phases. In the first phase, the SR technique applied test images are tested for vegetation detection and mapping the vegetation information in a region with fourteen vegetation metrics. In the second phase, similar testing is done without applying SR for input test images. The experiment revealed that test images using the SR technique yielded higher average values of 5 percent and 1.1 percent for the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), respectively, as compared to images tested using the non-SR technique.

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“…In comparison, hyperspectral images have a high spectral resolution but low spatial resolution; meanwhile, multispectral images have a low spectral resolution but high spatial resolution [ 6 ]. Another difference is that multispectral images acquire spectral signals with fewer discrete values of wavelength bands and hyperspectral images acquire quasi-continuous values (the spectral is less than 10 nm) [ 6 , 7 ]. Taking into account that the spectral information is richer for hyperspectral images, this offers more availability to see unknown aspects of objects.…”

Section: Introductionmentioning

confidence: 99%

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Bilius

Pentiuc

2020

Sensors

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Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.

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Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

Cited by 35 publications

References 34 publications

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Effectiveness of Super-Resolution Technique on Vegetation Indices

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

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