The pan-sharpening of satellite and UAV imagery for agricultural applications

Jenerowicz, Agnieszka; Woroszkiewicz, Małgorzata

doi:10.1117/12.2241645

Cited by 18 publications

(13 citation statements)

References 46 publications

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“…Crop classification using the results of the fusion of UAV and satellite data enables better crop classification results than a single data source [21,22]. The free access to Sentinel-2A data with a spatial resolution of up to 10 meters and high spectral resolution will undoubtedly provide great help for agricultural research in the future.…”

Section: Discussionmentioning

confidence: 99%

“…From the Jenerowicz's study [22] (fusing Landsat 8 OLI MS (30 m) and UAV (0.04 m), which is similar to Sentinel-2A (10 and 20 m) and UAV (0.03 m) used in our study), the Gram-Schmidt transformation is fast and easy to implement and generates fused images with high integration quality colour and spatial detail. The Gram-Schmidt (GS) transformation, introduced by Laben [34].…”

Section: Data Fusionmentioning

confidence: 99%

“…However, the high price of Worldview-2 data limits its applications. Landsat and UAV data fusion have also been found to help crop identification results [22]. In this study, the authors compared the methods of Gram-Schmidt and PCA method in fusing Landsat 8 OLI MS (30 m) and UAV (0.04 m).…”

Section: Introductionmentioning

confidence: 99%

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Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

et al. 2019

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Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

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Section: Discussionmentioning

confidence: 99%

Section: Data Fusionmentioning

confidence: 99%

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Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

et al. 2019

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“…Data integration is an important and widely developed concept due to the easy access to a large number of different data [1,2,3,4], mainly including image data obtained from the satellite and aerial ceiling [5,6,7] and from low altitudes [8,9,10]. The concept of pan-sharpening, which refers to the fusion of satellite imagery, is popular in remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

Sekrecka

Kędzierski

2018

Sensors

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Commonly used image fusion techniques generally produce good results for images obtained from the same sensor, with a standard ratio of spatial resolution (1:4). However, an atypical high ratio of resolution reduces the effectiveness of fusion methods resulting in a decrease in the spectral or spatial quality of the sharpened image. An important issue is the development of a method that allows for maintaining simultaneous high spatial and spectral quality. The authors propose to strengthen the pan-sharpening methods through prior modification of the panchromatic image. Local statistics of the differences between the original panchromatic image and the intensity of the multispectral image are used to detect spatial details. The Euler’s number and the distance of each pixel from the nearest pixel classified as a spatial detail determine the weight of the information collected from each integrated image. The research was carried out for several pan-sharpening methods and for data sets with different levels of spectral matching. The proposed solution allows for a greater improvement in the quality of spectral fusion, while being able to identify the same spatial details for most pan-sharpening methods and is mainly dedicated to Intensity-Hue-Saturation based methods for which the following improvements in spectral quality were achieved: about 30% for the urbanized area and about 15% for the non-urbanized area.

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“…Therefore, to integrate high spatial resolution data with a high spectral resolution, pansharpening algorithms are used [2]. They are used both for satellite and aerial data [3][4][5][6] and for data obtained from low altitudes [7,8]. The results of such image fusion are often used in urban and environmental analyses [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Grochala

Kędzierski

2017

Remote Sensing

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The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author's algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author's algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization.

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The pan-sharpening of satellite and UAV imagery for agricultural applications

Cited by 18 publications

References 46 publications

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

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