Gram-Schmidt Spectral Sharpening Research Articles

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high-spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView-2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this. Doi: 10.28991/ESJ-2023-07-04-013 Full Text: PDF

Utilization of multispectral landsat-8 remote sensing data for lithological mapping of southwestern Saudi Arabia

The upper plutonic rocks in southwestern Saudi Arabia has been mapped through Landsat 8 Operational Land Imager (OLI) data. The gathered field rock samples were subjected to spectral measurements (in the range of 0.4–2.5 µm) in the laboratory using a GER 3700 spectroradiometer. Integrated remote sensing techniques, including the band ratio, principal component analysis (PCA), and Gram-Schmidt spectral sharpening have been applied through this study. The red–greenblue composites of the pan-sharpened original bands (3, 4, and 7), principal components (PC3, PC1, and PC2), and band ratios 5/6 and 5/7 were found to be the most useful to delineate the study area’s Upper Proterozoic plutonic rocks. The results of image processing methods, spectral reflectance analyses are in good agreement with the field data. The spectral reflectance curves of the collected samples and the corresponding Landsat 8 pixels match well. It is indicated that Landsat-8 image data has great capability to identify lithological units and could be applicable in several areas in arid and semiarid environments.

Fusion of multispectral imagery and LiDAR data for roofing materials and roofing surface conditions assessment

ABSTRACT Assessment of rooftop rainwater harvesting (RRWH) quality and suitability requires detail and reliable information on roofs. Characterization of roof surface conditions affects the quality of harvested rainwater. Nevertheless, the implementation of the system requires improvement in terms of the roof detection techniques to ensure the roof of the building is selected appropriately. Thus, the classification techniques need to be optimized to detect roof materials and roof surface conditions (new or old) with high accuracy. This study aimed to produce high precision detailed roof materials and roof surface conditions map with using high-resolution remote sensing imagery, WorldView-3 (WV3) and light detection and ranging (LiDAR) data. Three different fusion methods; layer stacking (LS), Gram-Schmidt (GS) and principal components spectral sharpening (PCSS) were explored and their performances were compared to improve the spatial and spectral richness of the image. Subsequently, the roof materials and roof surface conditions classes which include old concrete, new concrete, old metal, new metal, old asbestos and new asbestos had been discriminated by employing support vector machine (SVM) and the rule-based technique known as a decision tree (DT). Generally, generated rule-sets present a higher overall accuracy with 87%, 72% and 66% for LS, GS and PCSS, respectively. For SVM classifier, the maximum accuracy recorded for LS, PCSS and GS were 70%, 63% and 43% respectively. Therefore, rule-based classification via LS fusion technique was utilized to identify suitable rooftops for the development of harvested rainwater system in the urban area. Findings indicate that the degradation status of a roof in heterogenous urban environments could be determined from satellite observation and the quality of roof-based harvested rainwater affected by roofing materials and roofing surface conditions can be analysed effectively.

Combining synthetic aperture radar and multispectral images for land cover classification: a case study of Beijing, China

We propose exploratory research combining synthetic aperture radar (SAR) data, represented by Sentinel-1A, and multispectral data, represented by Landsat-8 operational land imager (OLI), to demonstrate the applicability and effectiveness of land cover classification based on a Beijing case study. The proposed method consists of two phases. In the fusion phase, we select three methods to evaluate the performance of integrated Sentinel-1A and Landsat-8 OLI images. In the classification phase, we choose four common methods to examine the classifying capability hidden within the fused images. Experimental results indicate that the Gram-Schmidt spectral sharpening is superior in terms of maintaining the geometric structure, spectral texture, and spatial information, demonstrating a better fusion effect than other methods. The support vector machine classification exhibits the best performance of the fused images, with an overall classification accuracy of 94.01% and a kappa coefficient of 0.91. The fused images provide better classification potential as they benefit from having more spatial information and spectral information distribution, and the mean value of overall classification accuracy and the kappa coefficient are on average 5.61% and 0.08 higher, respectively, than the original Landsat-8. Finally, we conclude that the integrated use of SAR and multispectral images significantly improves classification accuracies, thus making it effective for land cover information extraction.

RESEARCH ON REMOTE SENSING IMAGE CLASSIFICATION BASED ON FEATURE LEVEL FUSION

Abstract. Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

ANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS

Image fusion is a process to generate higher spatial resolution multispectral images by fusion of lower resolution multispectral images and higher resolution panchromatic images. It is used to generate not only visually appealing images but also provide detailed images to support applications in remote sensing field, including rural area. The aim of this study was to evaluate the performance of SPOT-6 data fusion using Gram-Schmidt Spectral Sharpening (GS) method on rural areas. GS method was compared with Principle Component Spectral Sharpening (PC) method to evaluate the reliability of GS method. In this study, the performance of GS was presented based on multispectral and panchromatic of SPOT-6 images. The spatial resolution of the multispectral (MS) image was enhanced by merging the high resolution Panchromatic (Pan) image in GS method. The fused image of GS and PC were assessed visually and statistically. Relative Mean Difference (RMD), Relative Variation Difference (RVD), and Peak Signal to Noise Ratio (PSNR) Index were used to assess the fused image statistically. The test sites of rural areas were devided into four main areas i.e., whole area, rice field area, forest area, and settlement. Based on the results, the visual quality of the fused image using GS method was better than using PC method. The color of the fused image using GS was better and more natural than using PC. In the statistical assessment, the RMD results of both methods were similar. In the RVD results, GS method was better then PC method especially in band 1 and band 3. GS method was better than PC method in PSNR result for each test site. It was observed that the Gram-Schmidt method provides the best performance for each band and test site. Thus, GS was a robust method for SPOT-6 data fusion especially on rural areas.

An improved Landsat Image Mosaic of Antarctica

A revised Landsat Image Mosaic of Antarctica (LIMA) is presented, using the 1073 multi-band scenes of the original Land-sat-7 ETM+ LIMA image collection available at the United States Geological Survey (USGS: http://lima.usgs.gov/). Three improvements have been applied during the data processing: (1) DN saturation is adjusted by adopting a linear regression, which has a lower root mean square error than the ratio regression used by LIMA; (2) solar elevation angle is calculated using pixel-level latitude/longitude and the acquisition time and date of the central pixel of the scene, improving slightly upon the bilinear interpolation of the solar elevation angles of scene corners applied in LIMA; and (3) two additional image bands, Band 5 and Band 7, are sharpened using the panchromatic band (Band 8) and a Gram-Schmidt Spectral Sharpening algorithm to more easily distinguish snow, cloud and exposed rocks. The final planetary reflectance product is stored in 16-bit bands to preserve the full radiometric content of the scenes. A comparative statistical analysis among 12 sample regions indicates that the new mosaic has enhanced visual qualities, information entropy, and information content for land cover classification relative to LIMA.

COMPARISON OF THE SPECTRAL PROPERTIES OF PANSHARPENED IMAGES GENERATED FROM AVNIR-2 AND PRISM ONBOARD ALOS

Abstract. A considerable number of methods for pansharpening remote-sensing images have been developed to generate higher spatial resolution multispectral images by the fusion of lower resolution multispectral images and higher resolution panchromatic images. Because pansharpening alters the spectral properties of multispectral images, method selection is one of the key factors influencing the accuracy of subsequent analyses such as land-cover classification or change detection. In this study, seven pixel-based pansharpening methods (additive wavelet intensity, additive wavelet principal component, generalized Laplacian pyramid with spectral distortion minimization, generalized intensity-hue-saturation (GIHS) transform, GIHS adaptive, Gram-Schmidt spectral sharpening, and block-based synthetic variable ratio) were compared using AVNIR-2 and PRISM onboard ALOS from the viewpoint of the preservation of spectral properties of AVNIR-2. A visual comparison was made between pansharpened images generated from spatially degraded AVNIR-2 and original images over urban, agricultural, and forest areas. The similarity of the images was evaluated in terms of the image contrast, the color distinction, and the brightness of the ground objects. In the quantitative assessment, three kinds of statistical indices, correlation coefficient, ERGAS, and Q index, were calculated by band and land-cover type. These scores were relatively superior in bands 2 and 3 compared with the other two bands, especially over urban and agricultural areas. Band 4 showed a strong dependency on the land-cover type. This was attributable to the differences in the observing spectral wavelengths of the sensors and local scene variances.

ISVR: an improved synthetic variable ratio method for image fusion

An Improved Synthetic Variable Ratio (ISVR) fusion method is proposed to merge high spatial resolution panchromatic (Pan) images and multispectral (MS) images based on a simulation of the panchromatic image from the multispectral bands. Compared to the existing SVR (Synthetic Variable Ratio) family methods, the ISVR method manifests two major improvements: a simplified and physically meaningful scheme to derive the parameters necessary as required by SVR, and less computing power. Two sets of IKONOS Pan and MS images: one in urban area and another one in a forest area, were used to evaluate the effectiveness of classification-oriented ISVR method in comparison to the Principal Component Substitution (PCS), Synthetic Variable Ratio (SVR) and Gram-Schmidt Spectral Sharpening (GS) methods that are available in the ENVI software package. Results indicate the ISVR method achieves the best spectral fidelity to facilitate classification compared to PCS, SVR, and GS methods.