Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul

Sarp, Gulcan

doi:10.5721/eujrs20144702

Cited by 79 publications

(59 citation statements)

References 14 publications

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“…GE1 can simultaneously capture the PAN band (spectral range from 450 to 800 nm) and four MS bands such as Blue (B: 450 to 510 nm), Green (G: 510-580 nm), Red (R: 655-690 nm) and Near Infrared (NIR: 780-920 nm). It is worth noting that the higher geometric detail of a PAN image and the useful color information of a lower resolution MS image (four bands) can be integrated to produce a final pan-sharpened MS image with high spatial resolution by applying image fusion techniques [Sarp, 2014;Nikolakopoulos and Oikonomidis, 2015]. With the availability of pan-sharpened VHR satellite imagery, classification of small scale manmade structures in urban environments have become of great interest.…”

Section: Introductionmentioning

confidence: 99%

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

Aguilar

Fernández

Aguilar

et al. 2016

European Journal of Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

Aguilar

Fernández

Aguilar

et al. 2016

European Journal of Remote Sensing

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“…sharpening method was used to fuse the 0.5-m panchromatic image and 2-m multispectral image to produce a 0.5-m pan-sharpened image [26].…”

Section: Feature Selection Mangroves Classification and Image Segmementioning

confidence: 99%

“…Second, the radiance value was converted to surface reflectance using the FLAASH (fast line-of-sight atmospheric analysis of the spectral hypercubes) model in ENVI 5.3 (Harris Geospatial, Melbourne, FL, USA). Finally, the Gram-Schmidt spectral sharpening method was used to fuse the 0.5-m panchromatic image and 2-m multispectral image to produce a 0.5-m pan-sharpened image [26].…”

Section: Remote Sensing Data and Pre-processingmentioning

confidence: 99%

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

et al. 2018

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Abstract:In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms-decision tree (DT), support vector machine (SVM), and random forest (RF)-were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar's test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped.

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“…In order to combine the high spatial resolution of the panchromatic imagery and high spectral resolution of the multispectral imagery, image pairs were fused together using a Gram-Schmidt (GS) pan-sharpening algorithm. The GS algorithm was chosen because it preserves the spectral and spatial integrity of the original imagery [27]. Pan-sharpened images were classified using a Maximum Likelihood (ML) supervised classification algorithm to yield a binary output: ice (0) and pond (1).…”

Section: Data and Pre-processingmentioning

confidence: 99%

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

et al. 2017

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Abstract:The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (f p ) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two~5.2 km long profiles over FYI-and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring f p using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring f p . A stronger negative correlation was observed between FYI thickness and f p (Spearman r s = −0.85) compared to FYI roughness and f p (r s = −0.52). The association between MYI thickness and f p was also negative (r s = −0.56), whereas there was no association between MYI roughness and f p . 47% of spring f p variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high f p ) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low f p ). This work provides an important contribution towards the parameterizations of f p in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring f p .

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Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul

Cited by 79 publications

References 14 publications

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

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