Wyniki wyszukiwania - BazTech

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Satellite Image Fusion Using a Hybrid Traditional and Deep Learning Method

Hammad Mahmoud M., Mahmoud Tarek A., Amein Ahmed Saleh, Ghoniemy Tarek S.

Geomatics and Environmental Engineering

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2023

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Vol. 17, no. 5

145--162

EN

Due to growing demand for ground-truth in deep learning-based remote sensing satellite image fusion, numerous approaches have been presented. Of these approaches, Wald’s protocol is the most commonly used. In this paper, a new workflow is proposed consisting of two main parts. The first part targets obtaining the ground-truth images using the results of a pre-designed and well-tested hybrid traditional fusion method. This method combines the Gram–Schmidt and curvelet transform techniques to generate accurate and reliable fusion results. The second part focuses on the training of a proposed deep learning model using rich and informative data provided by the first stage to improve the fusion performance. The demonstrated deep learning model relies on a series of residual dense blocks to enhance network depth and facilitate the effective feature learning process. These blocks are designed to capture both low-level and high-level information, enabling the model to extract intricate details and meaningful features from the input data. The performance evaluation of the proposed model is carried out using seven metrics such as peak-signal-to-noise-ratio and quality without reference. The experimental results demonstrate that the proposed approach outperforms state-of-the-art methods in terms of image quality. It also exhibits the robustness and powerful nature of the proposed approach which has the potential to be applied to many remote sensing applications in agriculture, environmental monitoring, and change detection.

2

Estimation of Suspended Sediment Concentration in Downstream of the Ba River Basin using Remote Sensing Images

Nguyen Ba Dung, Bui Ngoc An, Dang Tuyet Minh

Inżynieria Mineralna

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2021

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no. 2

293--303

EN

Assessing the tendency of suspended sediment concentration (SSC) in the river watersheds enables a better understanding of the hydromorphological properties of its basins and the associated processes. In addition, analyzing this trend is essential to address several important issues such as erosion, water pollution, human health risks, etc. Therefore, it is critical to determine a proper method to quantify spatio-temporal variability in SSC. In recent years, remote sensing and GIS technologies are being widely applied to support scientists, researchers, and environmental resource investigators to quickly and synchronously capture information on a large scale. The combination of remote sensing and GIS data will become the reliable and timely updated data source for the managers, researchers on many fields. There are several tools, software, algorithms being used in extracting information from satellites and support for the analysis, image interpretation, data collection. The information from satellite images related to water resources includes vegetational cover, flooding events on a large scale, rain forecast, population distribution, forest fire, landslide movements, sedimentation, etc., and especially information on water quality, sediment concentration. This paper presents the initial result from LANDSAT satellite image interpretation to investigate the amount of sediment carried downstream of the Ba river basin.

3

Volcanic ash cloud detection from MODIS image based on CPIWS method

Liu L., Li Ch., Lei Y., Yin J., Zhao J.

Acta Geophysica

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2017

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Vol. 65, no. 1

151--163

EN

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.

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Mixture model and Markov random field-based remote sensing image unsupervised clustering method

Hou Y., Yang Y., Rao N., Lun X., Lan J.

Opto-Electronics Review

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2011

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Vol. 19, No. 1

83-88

EN

In this paper, a novel method for remote sensing image clustering based on mixture model and Markov random field (MRF) is proposed. A remote sensing image can be considered as Gaussian mixture model. The image clustering result corresponding to the image label field is a MRF. So, the image clustering procedure is transformed to a maximum a posterior (MAP) problem by Bayesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique are introduced into the traditional MRF potential function. The iterative conditional model (ICM) is employed to find the solution of MAP. We use the max entropy criterion to choose the optimal clustering number. In the experiments, the method is compared with the traditional MRF clustering method using ICM and simulated annealing (SA). The results show that this method is better than the traditional MRF model both in noise filtering and miss-classification ratio.

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Regiony geograficzne Polski

Olędzki J. R.

Teledetekcja Środowiska

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2007

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T. 38

1--337