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

Seo, Dae Kyo; Eo, Yang Dam

doi:10.3390/app9214543

Cited by 9 publications

(13 citation statements)

References 46 publications

(65 reference statements)

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“…The schematic of the MLP structure is shown in Figure 1, which illustrates a nonlinear mapping between the input vector and the output vector [35]. The neurons are connected through weights, and output signals are generated by a nonlinear transfer function [36].…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

et al. 2020

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Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

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Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

et al. 2020

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“…Conventional image-fusion methods use only the pixel values of SAR and panchromatic images. However, in general, the gray level of single pixels is not informative; therefore, additional information other than the pixel values is necessary [38,39]. To ensure that abundant information is considered, this study uses texture information.…”

Section: Feature Extractionmentioning

confidence: 99%

A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery

Seo

2020

Applied Sciences

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Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based; it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model’s ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.

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Section: Introductionmentioning

confidence: 99%

“…In general, RRN is classified into global statistical (GS) and radiometric control set sample (RCSS) methods [36,42]. The GS method uses the brightness value of all the pixels in the image [36,42], whereas the RCSS method uses the selected invariant features between images [8,39]. When the images exhibit similar radiometric characteristics, the GS method can rapidly and simply minimize the difference in brightness values between images [43,44].…”

Section: Introductionmentioning

confidence: 99%

Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction

Kim

Han

2021

Remote Sensing

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Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images owing to external environmental factors, and these errors cause various inaccuracies, thereby hindering the effective use of multitemporal VHR satellite images. Such errors can be minimized by applying preprocessing methods such as image registration and relative radiometric normalization (RRN). However, as the data used in image registration and RRN differ, data consistency and computational efficiency are impaired, particularly when processing large amounts of data, such as a large volume of multitemporal VHR satellite images. To resolve these issues, we proposed an integrated preprocessing method by extracting pseudo-invariant features (PIFs), used for RRN, based on the conjugate points (CPs) extracted for image registration. To this end, the image registration was performed using CPs extracted using the speeded-up robust feature algorithm. Then, PIFs were extracted based on the CPs by removing vegetation areas followed by application of the region growing algorithm. Experiments were conducted on two sites constructed under different acquisition conditions to confirm the robustness of the proposed method. Various analyses based on visual and quantitative evaluation of the experimental results were performed from geometric and radiometric perspectives. The results evidence the successful integration of the image registration and RRN preprocessing steps by achieving a reasonable and stable performance.

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

Cited by 9 publications

References 46 publications

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery

Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction

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