The PAN and MS image fusion algorithm based on adaptive guided filtering and gradient information regulation

Wang, Xianghai; Bai, Shifu; Li, Zhi; Sui, Yuanqi; Tao, Jingzhe

doi:10.1016/j.ins.2020.09.006

Cited by 15 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the estimation of the direction of the FC-GLCM for each trend category in the image L f , the number of pixels contained in each trend category is taken as the confidence of the trend direction, and adaptive trend fusion based on coefficient optimization is performed to obtain the final overall image trend ϕ * [42]. The process of adaptive trend fusion is shown in Figure 5.…”

Section: Adaptive Trend Fusionmentioning

confidence: 99%

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

Wang

Qiu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.

show abstract

Section: Adaptive Trend Fusionmentioning

confidence: 99%

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

Wang

Qiu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The process of combining images from multiple sources into a single imagery is referred to as multi-source image fusion technology, where the resulted fused image would be more beneficial than any of the input imageries, and it has major importance to the photogrammetry tasks in computer vision [26][27][28][29]. A detailed review can be found in [30].…”

Section: Sohn and Dowmanmentioning

confidence: 99%

A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo

Taha

Ibrahim

2022

GaEE

View full text Add to dashboard Cite

Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity.A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution.The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.

show abstract

“…In previous research and analysis, image restoration is often realized by super-resolution and image features are generated by noise blurred image sequences. is method is widely used in meteorology, medicine, and so on [2]. At present, there are many SR restoration algorithms, covering the frequency domain and spatial domain.…”

Section: Introductionmentioning

confidence: 99%

Research on Low-Resolution Image Fusion Algorithm Based on Deep Learning

Zhang

2022

Scientific Programming

View full text Add to dashboard Cite

The emergence of portable devices provides great convenience for image acquisition, but the image resolution is too low, which affects the identification and use of the image. Multiframe super-resolution algorithm makes a great contribution to extracting image features, but there is a problem with too much computation. Based on this, this paper proposes a low-resolution image fusion algorithm based on deep learning. Based on the introduction of extracting image features from low-resolution images, this paper proposes to fuse multiple low-resolution images, reduce the time of calculation error per frame, reduce the amount of calculation, extract feature information, and improve the alexnet network model to realize image extraction. The convolutional self-coding fusion network is used to realize image fusion and complete the reconstruction and fusion of low-resolution images. In the simulation analysis of the fusion algorithm, objective indexes such as network structure and loss function are selected to evaluate the effectiveness of the algorithm. This paper reduces the computational complexity of image acquisition and ensures that the amount of calculation can be reduced in each iteration. Analyze the effectiveness of the fusion algorithm, comprehensively evaluate the objective indicators, and verify the advantages of the algorithm through indicators such as gradient change and information entropy.

show abstract

The PAN and MS image fusion algorithm based on adaptive guided filtering and gradient information regulation

Cited by 15 publications

References 25 publications

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo

Research on Low-Resolution Image Fusion Algorithm Based on Deep Learning

Contact Info

Product

Resources

About