Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Seo, Dae Kyo; Kim, Yong Hyun; Eo, Yang Dam; Yong, Park， Wan; Park, Hyunchun

doi:10.3390/rs9111163

Cited by 52 publications

(39 citation statements)

References 56 publications

(84 reference statements)

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“…At this time, feature selection is independently performed for each band, since the features affecting each band are different [25]. Furthermore, as the number of input features increases, the complexity of the model and noise increases, and thus it is important to select optimal features [46,47].…”

Section: Selection Of the Spectral Indexmentioning

confidence: 99%

“…As mentioned above, the changes that are caused by the growth of vegetation are the most typical nonlinear changes in the radiometric characteristics. In other words, a nonlinear relationship is required in order to perform normalization for the radiometric differences as well as the phenological differences [25,26]. In this study, the nonlinear normalization is performed while using an MLP that is a representative algorithm for modeling nonlinear relationships [33,48].…”

Section: Phenological Normalization Based On Multilayer Perceptronmentioning

confidence: 99%

“…Furthermore, it increases the overall complexity of the algorithm and the training time [50]. Therefore, after considering the nature of the normalization, with limits of training time, only one hidden layer is considered [21,25]. As for the selection of the number of neurons for the hidden layer, the comparison experiments are conducted by setting the number of neurons to 2, 3, 4, 5, 6, 7, 8, 9, 10, and 15.…”

Section: Phenological Normalization Based On Multilayer Perceptronmentioning

confidence: 99%

“…However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical satellite imagery, which is the main source in change detection, is affected by clouds and atmospheric conditions, it is difficult to acquire images that meet the temporal requirements [27,28].…”

mentioning

confidence: 99%

“…In addition, since optical satellite imagery, which is the main source in change detection, is affected by clouds and atmospheric conditions, it is difficult to acquire images that meet the temporal requirements [27,28]. In such cases, images with phenological differences as well as radiometric differences should be utilized [25,26]. In other words, compositive normalizations, including phenological and radiometric properties, should be conducted.This study proposes a nonlinear relative radiometric normalization method while considering the phenological as well as radiometric properties for high-resolution satellite images in order to overcome these limitations.…”

mentioning

confidence: 99%

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Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Seo

2019

Applied Sciences

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Radiometric normalization is an essential preprocessing step that must be performed to detect changes in multi-temporal satellite images and, in general, relative radiometric normalization is utilized. However, most relative radiometric normalization methods assume a linear relationship and they cannot take into account nonlinear properties, such as the distribution of the earth's surface or phenological differences that are caused by the growth of vegetation. Thus, this paper proposes a novel method that assumes a nonlinear relationship and it uses a representative nonlinear regression model-multilayer perceptron (MLP). The proposed method performs radiometric resolution compression while considering both the complexity and time cost, and radiometric control set samples are extracted based on a no-change set method. Subsequently, the spectral index is selected for each band to compensate for the phenological properties, phenological normalization is performed based on MLP, and the global radiometric properties are adjusted through postprocessing. Finally, a performance evaluation is conducted by comparing the results herein with those from conventional relative radiometric normalization algorithms. The experimental results show that the proposed method outperforms conventional methods in terms of both visual inspection and quantitative evaluation. In other words, the applicability of the proposed method to the normalization of multi-temporal images with nonlinear properties is confirmed. Relative radiometric normalization, on the other hand, is an image-based normalization method that selects one image as a reference and matches the radiometric characteristics of a subject image [13,14]. That is, relative radiometric normalization is generally utilized since the multi-temporal images are normalized into common scales without extra parameters [12,15].Relative radiometric normalization can be classified into two categories: a global statistics-based method and a radiometric control set sample (RCSS)-based method. The former is performed by using statistical values of all pixels in the image, which includes histogram matching, minimum-maximum (MM), mean-standard deviation (MS), and simple regression (SR) [15,16]. In contrast, the latter is based on invariant pixels, known as RCSSs, which include dark set-bright set (DB), pseudo invariant features (PIF), and no-change sets (NC) [17][18][19][20]. At this time, except for histogram matching, normalization is performed through regression equations, while assuming a linear relationship between the pixels at the same position in each band [21]. However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical sa...

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Section: Selection Of the Spectral Indexmentioning

confidence: 99%

Section: Phenological Normalization Based On Multilayer Perceptronmentioning

confidence: 99%

Section: Phenological Normalization Based On Multilayer Perceptronmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Seo

2019

Applied Sciences

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Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning Algorithms, Explainable Artificial Intelligence, and Hyperparameter Optimization Techniques

Hosseinzadeh,

Kasiri,

Rezaei

2024

steel research int.

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In this study, a novel application of machine learning (ML) is introduced to pellet modeling in the intricate non‐catalytic gas–solid reaction of direct reduction of iron oxide in the steel industry. Twenty ML models are developed using four algorithms: multilayer perceptron neural networks (MLPNN), radial basis function neural network (RBFNN), support vector regression, and random forest (RF). Hyperparameter optimization is conducted using Bayesian algorithms, random search, and grid search. The optimum model achieves a mean squared error test of 0.0052 with random RF for the larger dataset (872 samples), while smaller datasets (132, 225, and 242 samples) produce optimum models with MLPNN and RBFNN. Hyperparameters vary between the larger datasets and the smaller datasets. The models offer insight into the complex interactions among variables, including time, temperature, gas composition, hematite composition, pellet radius, and initial pellet porosity, influencing the metallization degree. In this study, the significant role of time and temperature is emphasized, as revealed by explainable artificial intelligence using Shapley additive explanation analysis that utilizes the game theory, and the effects of pellet modeling parameters are elucidated through 3D plots, particularly highlighting the impact of changing H2/CO proportion on metallization degree and carbon deposit.

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Evaluating Land Cover Change on the Island of Santa Cruz, Galapagos Archipelago of Ecuador Through Cloud-Gap Filling and Multi-sensor Analysis

Shao

Wan

Rosenman

et al. 2020

Social and Ecological Interactions in the Galapagos Islands

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Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Cited by 52 publications

References 56 publications

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning Algorithms, Explainable Artificial Intelligence, and Hyperparameter Optimization Techniques

Evaluating Land Cover Change on the Island of Santa Cruz, Galapagos Archipelago of Ecuador Through Cloud-Gap Filling and Multi-sensor Analysis

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