• 항공우주기술
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• 제10권1호
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• pp.149-155
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• 2011

세계적 기후온난화와 이상기온현상으로 최근 급변하는 농업환경에 대응하기 위해서는 농작물 작황관리 및 예측시스템의 과학화를 통한 정부차원의 대처능력 개선이 시급하다. 농업분야에서 위성정보의 활용은 고해상도 광학 및 레이더 영상의 상용화와 더불어 정밀농업이라는 새로운 가능성을 열어주고 있다. 본 연구에서는 최근 농업분야에서 주목을 받고 있는 RapidEye 위성영상을 사용하여 우리나라 농촌지역의 토지이용유형별 분광식생지수의 시계열 특성을 살펴보았다. 식생과 비식생지역 간에 뚜렷한 시계열 변화양상이 나타났으며, 식생지역 내에서도 산림 수종별, 논 그룹별로 식생지수의 시계열 변화에 차이가 관찰되었다.

• Journal of Astronomy and Space Sciences
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• 제29권4호
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• pp.407-411
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• 2012

Space-borne remote sensing is an effective and inexpensive way to identify crop fields and detect the crop condition. We examined the multi-temporal spectral characteristics of rice fields in South Korea to detect their phenological development and condition. These rice fields are compact, small-scale parcels of land. For the analysis, moderate resolution imaging spectroradiometer (MODIS) and RapidEye images acquired in 2011 were used. The annual spectral tendencies of different crop types could be detected using MODIS data because of its high temporal resolution, despite its relatively low spatial resolution. A comparison between MODIS and RapidEye showed that the spectral characteristics changed with the spatial resolution. The vegetation index (VI) derived from MODIS revealed more moderate values among different land-cover types than the index derived from RapidEye. Additionally, an analysis of various VIs using RapidEye satellite data showed that the VI adopting the red edge band reflected crop conditions better than the traditionally used normalized difference VI.

• 방송공학회논문지
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• 제1권1호
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• pp.67-73
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• 1996

일반적으로 고음질 오디오 부호화 방법은 전통적인 데이터 압축 기법과 인간의청각 모델을 결합한 구조를 갖고 있다. 고음질 오디오 부호화에 사용되는 주요한 청각 특성은 주파수 영역에서의 마스킹 현상이므로 서브밴드 부호화나 변환 부호화와 같은 주파수 영역 방법들이 널리 사용된다[1][2]. 그러나 지금까지의 고음질 오디오 부호화에서 시간 영역 마스킹과 시간 영역 중복성을 제거하는 방법은 적용되지 않았다. 본 논문에서 제안한 오디오 데이터 압축 방법은 시간 및 주파수 영역에서 통계적, 지각적 중복성을 제거한다. 주파수 영역으로 변환된 오디오 신호는 6프레임으로 구성된 패킷으로 나뉘어진다. 한 패킷은 1536 샘플 ($256{\times}6$)로 되어 있으며 패킷 내에서의 중복성은 시간 및 주파수 영역에서 존재한다. 각 패킷에서 두 중복성이 동시에 제거되어진다. 심리음향 모델에 있어서도 세밀한 주파수 마스킹과 함께 시간 영역 마스킹을 고려하여 보다 정확한 결과를 얻을 수 있도록 향상되었다. 양자화를 위해서 각 패킷은 비선형적인 임계 대역과 시간적인 청각 특성을 반영할 수 있도록 설계된 부블럭으로 분할되었다. 따라서 낮은 비트율에서 고음질의 복원음을 얻을 수 있었다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 하계학술대회 논문집 Vol.4 No.2
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• pp.1094-1097
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• 2003

In this paper, the cavity-length-dependent spectral and temporal characteristics of a V-groove AlGaAs-GaAs quantum wire (QWR) laser at each subband were investigated. At short cavity lasers less than $300{\mu}m$, a discrete wavelength switching from the n=1 to the n=2 subband occurred due to the increased threshold gain, resulting from the increased cavity loss. Using the characteristic of the wavelength shift from n=1 to the n=2 subband with shortening the cavity length, ultrafast lasing behaviors under gain switching at the n=1 and the n=2 subband transition were demonstrated and compared.

• 대한의용생체공학회:의공학회지
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• 제27권4호
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• pp.170-179
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• 2006

The purpose of this study was to investigate the spatiotemporal cortical activation pattern and functional connectivity during visual perception of words. 61 channel recordings of electroencephalogram were obtained from 15 subjects while they were judging the meaning of Korean, English, and Chinese words with concrete meanings. We examined event-related potentials (ERP) and applied independent component analysis (ICA) to find and separate simultaneously activated neural sources. Spectral analysis was also performed to investigate the gamma-band activity (GBA, 30-50 Hz) which is known to reflect feature binding. Five significant ERP components were identified and left hemispheric dominance was observed for most sites. Meaningful differences of amplitudes and latencies among languages were observed. It seemed that familiarity with each language and orthographic characteristics affected the characteristics of ERP components. ICA helped confirm several prominent sources corresponding to some ERP components. The results of spectral and time-frequency analyses showed distinct GBAs at prefrontal, frontal, and temporal sites. The GBAs at prefrontal and temporal sites were significantly correlated with the LPC amplitude and response time. The differences in spatiotemporal patterns of GBA among languages were not prominent compared to the inter-individual differences. The gamma-band coherence revealed short-range connectivity within frontal region and long-range connectivity between frontal, posterior, and temporal sites.

• 한국환경복원기술학회지
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• 제8권2호
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• pp.33-40
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• 2005

The objective of this research was to find an indirect method to estimate spectral reflectance and NDVI(Normalized Difference Vegetation Index) efficiently, using the spectroradiometer and NOAA AVHRR satellite data. For collecting RS base data, used spectro-radiometer that measures reflection characteristics between 300~1,100nm was used and measured the reflection of vegetation from paddy rice during the growing season at Chungbuk national university's farm in 2002. The feasibility of detecting the temporal variation in the spectral reflectance and NDVI in paddy rice were conducted on eight growth stages. AVHRR data were collected in eight different months over a one year period in 2002. The results were compared with those obtained by analyzing NDVI characteristics. The spectral reflectance and NDVI of paddy rice have a great effect on the growth condition. Considerably, NDVI was increased by developing muscle fiber tissue at the near infrared wavelength until the Booting stage. Then the NDVI increased until the Maturity stage and then decreased until harvest. The highest month was at July and the lower month was at March. The difference NDVI analysis using March and another months data was conducted, the results were provided information on the growth condition of crops.

• 한국환경과학회지
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• 제15권4호
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• pp.373-378
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• 2006

The western coast of South Korea is famous for its large and broad tidal lands. Nevertheless, land reclamation, which has been conducted on a large scale, such as Sihwa embankment construction project has accelerated coastal environmental changes in the embankment inland. For monitoring of environmental change, vegetation change detecting of the embankment inland were carried out and field survey data compared with Landsat TM, ETM+, IKONOS, and EOC satellite remotely sensed data. In order to utilize multi-temporal remotely sensed images effectively, all data set with pixel size were analyzed by same geometric correction method. To detect the tidal land vegetation change, the spectral characteristics and spatial resolution of Landsat TM and ETM+ images were analyzed by SMA(spectral mixture analysis). We obtained the 78.96% classification accuracy and Kappa index 0.2376 using March 2000 Landsat data. The SMA(spectral mixture analysis) results were considered with comparing of vegetation seasonal change detection method.

• 대한원격탐사학회지
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• 제19권5호
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• 전자통신동향분석
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• 제8권2호
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• pp.69-91
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• 1993

In the conventional speech processing, the technique of FFT(Fast Fourier Transform) is usually applied to the finite number of samples within the window of specified length using the fixed sampling rate. In this case, the temporal resolution is dependent upon the length of window while the spectral resolution is dependent upon the number of samples within the window. Thus, once the temporal resolution is determined the spectral resolution is also determined or vice versa. To resolve this type of dilemma, a new type of bank-filter similar to the characteristics of cochlear model needs to be considered. Furthermore, wide dynamic range of cochlea certainly helps the stable extraction of speech features. In the paper, the human auditory system will be briefly introduced and previous works on auditory preprocessors based on cochlear model will be reviewed. As a conclusion, the design considerations of auditory preprocessors based on cochlear model will be addressed.

• 대한원격탐사학회지
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• 제37권4호
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.