Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery

Pushparaj, Jagalingam; Hegde, Arkal Vittal

doi:10.1007/s12517-017-2878-3

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…All these bands (blue, green, red and NIR) were combined into to a single image using layer stacking in ENVI (Version 5.3) for further processing. For L8 data to be compared with S2 data with close spatial resolution, pan-sharpening process has been implemented to ensure that the composite image of L8 has spatial resolution of 15 m (Pushparaj & Hegde, 2017). In this case, ESRI algorithm (pan-sharpening method -ESRI, Zhang & Mishra, 2014) based on spectral modelling for data fusion has been utilized to sharpen the image.…”

Section: Pre-processingmentioning

confidence: 99%

The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method

Labib

Harris

2018

European Journal of Remote Sensing

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Green infrastructure (GI) mapping and monitoring is crucial in urban areas, and remote sensing is widely used to accomplish the task. Improved moderate resolution Sentinel-2A (10 m) and LandSat-8 (15 m) images, in place of commercial satellite images, enable GI mapping with little to no cost. Considering so, the objective of this paper is to evaluate the potential of GI feature extraction of Sentinel-2A (S2) and LandSat-8 (L8) (freely available images) using the Object Based Image Analysis (OBIA) method. The advantage of using OBIA over pixel-based analysis has been investigated primarily with very high resolution images. Using OBIA, bottom up (i.e. Multiresolution) and top down (i.e. Spectral Difference) segmentation were implemented using eCognition to obtain image objects for both S2 and L8 images. Then, rule-based classification was performed to extract GI areas from the objects. NDVI, NDWI, NIR/R ratios were utilized in rule set development, after several trial and error process. Both S2 and L8 provided acceptable extraction of GI for urban areas. However, with an overall accuracy of 71.24%, S2 was more effective when extracting GI areas. Shadows along roads and high rise buildings caused some inaccuracy in classification.

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Section: Pre-processingmentioning

confidence: 99%

The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method

Labib

Harris

2018

European Journal of Remote Sensing

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“…Usually, the Brovey transform, SVR, and RE have been developed to visually increase contrast in the low and high ends of an image's histogram. In general, BT is a combination of arithmetic operations and it needs to normalize the spectral bands before they are multiplied with the PAN band [26,27].…”

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confidence: 99%

Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

Rahaman

Hassan

Ahmed

2017

IJGI

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Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i)

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“…Gram-Schmidt sharpening uses the intensity image from a multiple regression analysis [27]. Most image fusion methods can be approximately summarized in two steps: (1) extract high-frequency spatial information from the PAN image, and (2) inject spatial details into a resized MS image by using different models [28]. In general, a framework can be defined as [29] …”

Section: Cross Fusion-based Change Detection Methodsmentioning

confidence: 99%

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Wang

Choi

et al. 2017

Remote Sensing

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Abstract:Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method-a measure for the spectral distortion of change information-is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

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Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery

Cited by 13 publications

References 31 publications

The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method

The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method

Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

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