A Novel Image Fusion Method for Water Body Extraction Based on Optimal Band Combination

Xiao, Xiaohong; Xie, Jiangang; Jiping, Niu; Cao, Wei

doi:10.18280/ts.370205

Cited by 6 publications

(4 citation statements)

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“…While scholars have demonstrated that image fusion processing improves image quality and increases image classification accuracy, the literature also reveals that different fusion techniques have offered varied enhancement processing characteristics. Thus, the choice of technique should take into consideration the image sensor type characteristics and the purpose for image classification [18][19][20][21][22][23][24][25]. Regarding images that have originated from various sensor types, many studies have been carried out to identify the effectiveness of fusion techniques from the aspect of spectral and spatial fidelity.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, specific thematic application purposes can affect the classification or interpretation of images; the requirements for image enhancement may also differ [25,[28][29][30][31][32]. In the context of using a fused image for the extraction of vegetation information in mountainous areas, the requirement for highly accurate spatial fidelity and spatial structure of the vegetation area image becomes much greater due to the large degree of similarity of spectral characteristics between different vegetation types.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Chen¹,

Fei²,

Wang³

et al. 2022

Sustainability

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This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Chen¹,

Fei²,

Wang³

et al. 2022

Sustainability

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show abstract

“…In recent years, different data sources, various tools, and methods have been used in water resource fields. Satellite Remote Sensing (RS) images accessibility compared to limited ground-based observations made it a cost-effective data source for water resources applications [4][5][6][7][8][9][10][11]. The various temporal and spatial resolutions of satellite images make it possible to investigate and monitor water resources phenomena, water level changes, and water resources crisis management [9].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Prediction of the Urmia Lake Drying Trend Based on Time-Series Remotely Sensed Images and Artificial Neural Networks

Sadeghi¹

2022

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Urmia Lake, which is the second largest permanent hypersaline lake in the world, is shrinking in recent decades. Since accurate spatial information about the lake is essential to managing the current and emerging crises of the lake, this study is used Urmia Lake satellite images time-series to investigate drought trends by analyzing via Artificial Neural Networks (ANN). The proposed approach is comprising the following four steps. First, yearly time-series Landsat images (2000-2022) are corrected geometrically and radiometrically. Then, time-series images of 2000-2020 are classified into five land cover classes, including; deep water, shallow water, salt, soil, and vegetation. In the third step, ANN trained for 2000-2019 as input and tested for 2020 as an output. Finally, the trained ANN is proposed to predict the future land covers of the lake (for 2021 and 2022 years). In order to evaluate the proposed model, the predicted maps of 2021 and 2022 were compared with their corresponding ground truth maps and quantitative criteria were calculated. The overall accuracy of the prediction for 2021 and 2022 is equal to 92.75% and 90.62%, respectively, which indicates the high capability of the proposed method for modeling and predicting changes in Urmia Lake and its shores.

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“…Real scenes are usually expressed in the form of analog signals, but now, with the rapid development of computer technology, the analog signals in the original images obtained can be digitally analyzed and processed [4,5]. The image resolution supported by current shooting and display equipment is getting higher and higher, and high-resolution images are bound to be the mainstream in the image field in the future [6]. Some researchers have applied the heterogeneous parallel acceleration technology in the field of dynamic image processing to effectively improve the processing speed of the algorithm with regard to high-resolution images, and also combined it with the denoising technology and high dynamic range processing technology to accelerate the recording of the dynamic range imaging [7].…”

Section: Introductionmentioning

confidence: 99%

Application of Dynamic Image Fusion Technology in Development of Innovation Capabilities of College Students Majoring in Art Design

Wang

2021

Int. J. Emerg. Technol. Learn.

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Art design is based on art and images, in which dynamic images require pre-processing and fusion. This paper explores how to apply the dynamic image fusion technology in the development of the innovation capabilities of college students majoring in art design. The research results show that image sequences with different exposures can be directly formed into low dynamic range images through multi-exposure fusion, or the high dynamic range can be restored first, and then converted to a low dynamic range image through tone mapping. The main principles for innovation development in art design education include conformity, student-centeredness, and overall optimization. The dynamic image fusion enhancement algorithm is mainly divided into five functional modules, which are used to calculate multi-scale gradient, structure tensor, and target gradient for the fused image, solve the steepest descent method and output normalized images, respectively

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A Novel Image Fusion Method for Water Body Extraction Based on Optimal Band Combination

Cited by 6 publications

References 0 publications

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Monitoring and Prediction of the Urmia Lake Drying Trend Based on Time-Series Remotely Sensed Images and Artificial Neural Networks

Application of Dynamic Image Fusion Technology in Development of Innovation Capabilities of College Students Majoring in Art Design

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