Influence of pansharpening techniques in obtaining accurate vegetation thematic maps

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

doi:10.1117/12.2241501

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…A supervised classification technique was applied only in the shrubland ecosystem scene in order to analyze the influence of the different pansharpening techniques in the generation of thematic maps [ 38 ]. The first step was to determine the classes appearing in the image and obtain a sufficient number of training samples.…”

Section: Methodsmentioning

confidence: 99%

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Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Ibarrola-Ulzurrun

Gonzalo-Martín

Marcello

et al. 2017

Sensors

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Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources' reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet 'à trous' through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet 'à trous' through fractal dimension maps as the best fusion algorithm for this ecosystem.

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Section: Methodsmentioning

confidence: 99%

“…Thus, it will analyze the influence of the fusion on the classification results which serve to obtain accurate information for the conservation of natural resources. This study can be found in more detail in [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Ibarrola-Ulzurrun

Gonzalo-Martín

Marcello

et al. 2017

Sensors

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“…1. Pansharpening has become a preprocessing step of image enhancement in many remote sensing tasks, such as object detection [7], [8], anomaly detection [9], [10] and agricultural management [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Differential Information Residual Convolutional Neural Network for Pansharpening

Jiang

Yuan

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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Pansharpening is a process of fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image to create a high-resolution multispectral image. A novel single-branch, single-scale lightweight convolutional neural network, named SDRCNN, is developed in this study. By using a novel dense residual connected structure and convolution block, SDRCNN achieved a better trade-off between accuracy and efficiency. The performance of SDRCNN was tested using four datasets from the WorldView-3, WorldView-2 and QuickBird satellites. The compared methods include eight traditional methods (i.e., GS, GSA, PRACS, BDSD, SFIM, GLP-CBD, CDIF and LRTCFPan) and five lightweight deep learning methods (i.e., PNN, PanNet, BayesianNet, DMDNet and FusionNet). Based on a visual inspection of the pansharpened images created and the associated absolute residual maps, SDRCNN exhibited least spatial detail blurring and spectral distortion, amongst all the methods considered. The values of the quantitative evaluation metrics were closest to their ideal values when SDRCNN was used. The processing time of SDRCNN was also the shortest among all methods tested. Finally, the effectiveness of each component in the SDRCNN was demonstrated in ablation experiments. All of these confirmed the superiority of SDRCNN.

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Advanced Classification of Remote Sensing High Resolution Imagery. An Application for the Management of Natural Resources

Ibarrola-Ulzurrun

Marcello

Gonzalo-Martín

2017

Developments and Advances in Intelligent Systems and Applications

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Influence of pansharpening techniques in obtaining accurate vegetation thematic maps

Cited by 4 publications

References 18 publications

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Differential Information Residual Convolutional Neural Network for Pansharpening

Advanced Classification of Remote Sensing High Resolution Imagery. An Application for the Management of Natural Resources

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