• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.515-523
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• 2018

In order to obtain consistent change detection result for multi-temporal satellite images, preprocessing must be performed. In particular, the preprocessing related to the spectral values can be performed by the radiometric normalization, and relative radiometric normalization is generally utilized. However, most relative radiometric normalization methods assume a linear relationship between the two images, and nonlinear spectral characteristics such as phenological differences are not considered. Therefore, this study proposes a relative radiometric normalization which assumes nonlinear relationships that can perform compositive normalization of radiometric and phenological characteristics. The proposed method selects the subject and reference images, and then extracts the radiometric control set samples through the no-change method. In addition, spectral indexes as well as pixel values are extracted in order to consider sufficient information, and modeling of nonlinear relationships is performed through multilayer perceptron. Finally, the proposed method is compared with the conventional relative radiometric normalization methods, which shows that the proposed method is visually and quantitatively superior.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.323-330
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• 2019

Radiometric normalization with multi-temporal satellite images is essential for time series analysis and change detection. Generally, relative radiometric normalization, which is an image-based method, is performed, and histogram matching is a representative method for normalizing the non-linear properties. However, since it utilizes global statistical information only, local information is not considered at all. Thus, this paper proposes a histogram matching method considering local information. The proposed method divides histograms based on density, mean, and standard deviation of image intensities, and performs histogram matching locally on the sub-histogram. The matched histogram is then further partitioned and this process is performed again, iteratively, controlled with the wasserstein distance. Finally, the proposed method is compared to global histogram matching. The experimental results show that the proposed method is visually and quantitatively superior to the conventional method, which indicates the applicability of the proposed method to the radiometric normalization of multi-temporal images with non-linear properties.

• Land and Housing Review
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• v.4 no.1
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• pp.125-131
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• 2013

To predict the real bearing capacity and settlement of the shallow foundation the plate load test results were used. But there is no field estimation method about igneous weathered soil and rock. Therefore, to predict the settlement equation, the plate load test about igneous weathered soil and rock was done in this study. To analyze the load ~ relative settlement curve by normalization, it did not use normal analysis method, but the load ~ relative settlement (s/B, s : settlement, B : breadth of plate) was used. As a result of normalization by load ~ relative settlement conception, the curve was regular regardless of plate diameter and it was suggested the relationship of in-situ soil condition and results.

• Speech Sciences
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• v.11 no.1
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.142-143
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• 2013

Green building certifications and environmental assessments are extensively implemented and studied to decrease the environmental impact during the life cycle of buildings. However, most of them are not appropriate to assess the environmental performance during the construction phase due to the difference of environmental factors. To develop an environmental index of construction projects, normalization should be conducted to compare the relative impact of each factor. As a first step, this study deduced normalization references of 4 environmental factors : noise, waste, greenhouse gas, and dust.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.105-127
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• 2024

We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.69-75
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• 2011

This paper investigates phrase positional effects on the timing of F0 (pitch) peaks in Tokyo Japanese disyllabic words with varying accent type (HL or LH) and phrase position (final or non final). The F0 peak timing was normalized by the total word duration ('normalized H timing'). The normalized H timing was significantly affected by accent type and phrase position. The H timing was later in the LH accent type than in the HL accent type, and in non final positions than in final positions. In addition, to examine the validity of the quantitative results, different models of phrase position effects were compared by measuring H timing in two approaches: normalization versus relative distance measures. For the normalization measures, the H timing was measured as the time of the F0 peak divided by the total word duration or by the duration of the tone bearing syllable. For the relative distance measures, the H timing was measured as the distance in milliseconds from the end of the word or from the end of the associated syllable. The best model was the normalization by the total word duration, rather than by the duration of the tone bearing syllable. This means that phrase positional effects on the timing of F0 peaks in Japanese disyllabic words are best modeled in terms of proportion of the total word duration.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.41-49
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• 2022

Documents often contain relative time expressions, and it is important to define a schema of the relative time information and develop a system that extracts such information from corpus. In this study, to deal with the relative time expressions, we propose seven additional attributes of timex3: year, month, day, week, hour, minute, and second. We propose a way to represent normalized values of the relative time expressions such as before, after, and count, and also design a set of rules to extract the relative time information from texts. With a new corpus constructed using the new attributes that consists of dialog, news, and history documents, we observed that our rule-set generally achieved 70% accuracy on the 1,041 documents. Especially, with the most frequently appeared attributes such as year, day, and week, we got higher accuracies compared to other attributes. The results of this study, our proposed timex3 attributes and the rule-set, will be useful in the development of services such as question-answer systems and chatbots.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1179-1185
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• 2017

The KOMPSAT-3A satellite acquires high-resolution MWIR images twice a day compared to conventional Earth observing satellites. New radiometric information of Earth's surface can be provided due to different characteristics from existing SWIR images or TIR images. In this study, the difference image of multitemporal images was generated and compared with existing infrared images to find the characteristics of KOMPSAT-3A MWIR satellite images. A co-registration was performed and the difference between pixel values was minimized by using PIFs (Pseudo Invariant Features) pixel-based relative normalization. The experiment using Sentinel-2 SWIR image, Landsat 8 TIR image, and KOMPSAT-3A MWIR image showed that the distinction between artifacts in the difference image of KOMPSAT-3A is prominent. It is believed that the utilization of KOMPSAT-3A MWIR images can be improved by using the characteristics of IR image.