A genetic algorithm solution to the gram-schmidt image fusion

Yılmaz, Volkan; Yilmaz, Cigdem Serifoglu; Güngör, Oğuz; Shan, Jie

doi:10.1080/01431161.2019.1667553

Cited by 37 publications

(31 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) Landsat 8 OLI feature extraction: to obtain high-quality optical data, the Gram-Schmidt image-fusion method [31,32] was used to fully utilize the spatial texture information via a panchromatic camera and the spectral information from the multispectral camera. On this basis, we calculated spectral features such as brightness value (BG), NDVI; gray-level co-occurrence matrix (GLCM) texture features, contrast, homogeneity, angular second moment, entropy,…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…The optical and SAR texture information was superimposed with a certain weight, the spectral information of the first principal component of the optical image was enhanced by a specific weight, and then we added the features of the optical image to the first principal component of the SAR image in order to obtain the enhanced principal component. Then a local energy fusion (1) Landsat 8 OLI feature extraction: to obtain high-quality optical data, the Gram-Schmidt image-fusion method [31,32] was used to fully utilize the spatial texture information via a panchromatic camera and the spectral information from the multispectral camera. On this basis, we calculated spectral features such as brightness value (BG), NDVI; gray-level co-occurrence matrix (GLCM) texture features, contrast, homogeneity, angular second moment, entropy, mean, dissimilarity, variance, and correlation.…”

Section: Proposed Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

et al. 2020

View full text Add to dashboard Cite

Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.

show abstract

Section: Proposed Frameworkmentioning

confidence: 99%

Section: Proposed Frameworkmentioning

confidence: 99%

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The orthogonal vectors are obtained as;

a_{1} = b_{1}

a_{2} = b_{2} - {Proj}_{a_{1}} false(b_{2} false)

a_{3} = b_{3} - {Proj}_{a_{1}} false(b_{3} false) - {Proj}_{a_{2}} false(b_{3} false)

c_{m} = \frac{a_{m}}{‖ a_{m} ‖}

where, a ∣ b is the inner product of a and b ; a 1 , a 2 … a m stand for the normalized orthogonal vectors and c 1 , c 2 … c m define the ortho‐normalized vectors 29,36 …”

Section: Svr and Gs Pansharpening Methodsmentioning

confidence: 99%

“…Hence, this study proposed to use the Non‐Dominated Sorting Genetic Algorithm‐II (NSGA‐II) metaheuristic algorithm to estimate the optimal band weights that ensure the optimal color and spatial structure fidelity in the CS‐based techniques. Actually, very few studies conducted the conventional Genetic Algorithm (GA) to enhance the performances of the pansharpening methods such as the Generalized IHS, 26 IHS, 27,28 GS, 29 and SVR 30 . These studies aimed to increase the color quality with a single‐objective GA. On the other hand, a successful pansharpening framework should retain the color features while sharpening the images, which makes the pansharpening process a multi‐objective problem.…”

Section: Introductionmentioning

confidence: 99%

A Non‐Dominated Sorting Genetic Algorithm‐II‐based approach to optimize the spectral and spatial quality of component substitution‐based pansharpened images

Yılmaz

2020

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary Pansharpening aims to fuse a lower‐resolution multispectral (MS) image and a higher‐resolution panchromatic image, resulting in an image with the color quality of the former and spatial detail quality of the latter. Of all, the component substitution (CS)‐based pansharpening methods have drawn attentions with their ability to produce sharp images. Despite their success in sharpening the images, these methods deteriorate the color features of the input MS images due of the uncertainty in the calculation of the intensity component used by them. Previous studies showed that attempts to preserve the color features tend to cause spatial detail loss to a certain extent. This, of course, reveals the necessity of a compromise between the spectral and spatial fidelity of the pansharpened images produced by the CS‐based techniques. This study proposed using the multi‐objective Non‐Dominated Sorting Genetic Algorithm‐II metaheuristic algorithm with the CS‐based methods to optimize the intensity component to find the best compromise between the spectral and spatial fidelity of the pansharpened images. The proposed framework was applied on two commonly used pansharpening techniques, Gram‐Schmidt and Synthetic Variable Ratio. It was found that the proposed methods managed to find the best balance between the color and spatial fidelity.

show abstract

“…The quality of the sharpened image varies according to the method used to calculate the injection gain g n , and there is a trade-off relationship between spatial and spectral accuracy. Pansharpening techniques can be divided into CS-and MRA-based techniques based on the method used to produce the low-spatial-resolution image I L [24,25]. However, to sharpen satellite images that do not provide panchromatic images, it is necessary to produce artificial panchromatic images.…”

Section: Optimal High-spatial-resolution Image Generationmentioning

confidence: 99%

Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

et al. 2020

View full text Add to dashboard Cite

Compared to using images in the visible and near-infrared (VNIR) wavelength range only, remotely sensed satellite imagery from the spectral wavelengths of both VNIR and shortwave infrared (SWIR), such as Sentinel-2A and Worldview-3, is more effective for analyzing various types of information for tasks such as land cover mapping, environmental monitoring and land use change detection. In this manuscript, a new sharpening technique to enhance the spatial resolution of Worldview-3 satellite imagery with various spatial and spectral resolutions is proposed. Selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the Gram-Schmidt adaptive (GSA) and Gram-Schmidt 2 (GS2) techniques, which are component substitution (CS)- and multiresolution analysis (MRA)-based algorithms, respectively. In addition, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. Qualitative and quantitative evaluation of the sharpened images showed that the pansharpening performance using the GS2 technique based on the selected band scheme and spectral distortion reduction was the best. To confirm the usability of the SWIR band, supervised classification based on machine learning was performed on the pansharpened images obtained by applying the technique proposed in this study and on the pansharpened images obtained by the VNIR bands only. The classification accuracy of the results using SWIR bands was higher than that of VNIR bands only. In particular, it was confirmed that the accuracy of the classification of artificial facilities known to be effective for SWIR bands was greatly improved.

show abstract

A genetic algorithm solution to the gram-schmidt image fusion

Cited by 37 publications

References 75 publications

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

A Non‐Dominated Sorting Genetic Algorithm‐II‐based approach to optimize the spectral and spatial quality of component substitution‐based pansharpened images

Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

Contact Info

Product

Resources

About