Multispectral to Panchromatic Image Fusion Based on Morphological Extended-Half-Gradient

Pandit, Vaibhav R.; Bhiwani, R. J.

doi:10.1007/s12524-020-01127-2

Cited by 5 publications

(4 citation statements)

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“…8 The combination of low spatial and high spectral imagery from satellites and high spatial and low spectral imagery from UAVs takes advantage of crop classification at the plant level. 7,[9][10][11] UAV RGB sensor captures visible bands with varying resolutions depending on the sensor characteristics and altitude of the UAV, and usually depicts vegetation in green, which prevents differentiation between different types of vegetation. 5,12 Different plant species have different spectral signatures at different wavelength regions, which is useful in classifying crops.…”

Section: Introductionmentioning

confidence: 99%

“…3,5 Various combinations of multispectral satellite imagery and UAV imagery bands have been shown to provide valuable information for classification of crops. 7,[9][10][11] Image fusion is a method that involves integrating multiple sources of images to enhance image quality and interpretability. This process facilitates improved decision-making across diverse fields, including but not limited to remote sensing, medical imaging, surveillance, and beyond.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of low spatial and high spectral imagery from satellites and high spatial and low spectral imagery from UAVs takes advantage of crop classification at the plant level 7 , 9 – 11 …”

Section: Introductionmentioning

confidence: 99%

“…Various combinations of multispectral satellite imagery and UAV imagery bands have been shown to provide valuable information for classification of crops 7 , 9 – 11 …”

Section: Introductionmentioning

confidence: 99%

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Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification

Allu,

Mesapam

2024

J. Appl. Rem. Sens.

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Crop classification is necessary to extract information about the crop, such as its type, the area in crop that may be used to estimate health, and the yield of the crop. Remote sensing is a technique used to extract earth surface information from satellite and aerial imagery. However, to extract crop parameters accurately requires high spatial and spectral information of the data, which can be achieved through image fusion. We focus on crop classification by selecting the suitable band combination of satellite imagery and unmanned aerial vehicle (UAV) data through image fusion technique. Various fusion techniques are available to produce high spatial and spectral resolution data. Principal component analysis PAN sharpening method is used to fuse Sentinel-2A and UAV data for crop classification, and the best combinations of different bands are assessed based on the image quality metrics and classification accuracy. Random forest classification algorithm is performed to classify the fused images and the classification accuracy is assessed by using the F -score, Kappa coefficient, and overall accuracy. The best accuracy of 93.14% and a kappa value of 0.87 is achieved using random forest classification technique using fused images of red band of UAV imagery with BGRNIR (blue, green, red, and NIR) bands of Sentinel-2A imagery is high compared to other band combinations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Various combinations of multispectral satellite imagery and UAV imagery bands have been shown to provide valuable information for classification of crops 7 , 9 – 11 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations