• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.959-962
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• 2006

Public education is undoubtedly a very important aspect for a country to develop space program. People have the rights to understand how the tax they paid is being used. This paper addresses the effectiveness of FORMOSAT-2 on public education in Taiwan. As the first remote sensing satellite of the National Space Organization (NSPO) of Taiwan, FORMOSAT-2 is a small satellite of 746 kg mass for two remote sensing missions: Earth and upward lightning observations. The mission orbit is sun-synchronous of 888 km altitude for exactly 14 revolutions per day. For earth observation, the payload is an advanced high resolution remote sensing instrument (RSI) with ground sampling distance (GSD) 2 m in panchromatic (PAN) band and 8 m in four multi-spectral (MS) bands. For upward lightning observation, the payload is an imager of sprites and upper atmospheric lightning (ISUAL). After more than two years of Earth observation started in June 2004, the effectiveness of FORMOSAT-2 images on public education in Taiwan is very promised. Five domestic universities and one private company in Taiwan have signed contracts respectively with NSPO to take the roles of satellite image investigator and distributor. A private company has signed contract with NSPO to generate and provide URMAP (= your map) in its website for general public applications by using FORMOSAT-2 images. The Newtonkids Book Company used FORMOSAT-2 images to publish a kind of calendar for children education purpose. Besides, a science team in National Cheng Kung University (NCKU) is doing the research work on the 3820 (up to 30 June 2006) transient luminous events (TLEs) observed by FORMOSAT-2.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.142-145
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• 2005

Since the successful launch of FORMOSAT -2 satellite by National Space Organization of Taiwan in May 2004, the Remote Sensing Instrument (RSI) on- board the FORMOSAT -2 has continuously acquired images at one panchromatic and four multi-spectral bands (http://www.nspo.org.tw). In general, the RSI performs well and receives high quality images which proved to be very useful for various applications. However, some RSI panchromatic products exhibit obvious trembling artifact that must be removed. Preliminary study reveals that the trembling artifact is caused by the instability of the spacecraft attitude. Though the magnitude of this artifact is actually less than half of a pixel, it affects the applicability of panchromatic products. A procedure removing this artifact is therefore needed for providing image products of consistent quality. Due to the nature of trembling artifact, it is impossible to describe the trembling amount by employing an analytic model. Relied only on image itself, an algorithm determining trembling amount and removing accordingly the trembling artifact is proposed. The algorithm consists of 3 stages. First, a cross-correlation based scheme is used to measure the relative shift between adjacent scan lines. Follows, the trembling amount is estimated from the measured value. For this purpose, the Fourier transform is utilized to characterize random shifts in frequency domain. An adaptive estimation method is then applied to deduce the approximate trembling amount. In the subsequent stage, image re-sampling operation is applied to restore the trembling-free product. Experimental results show that by applying the proposed algorithm, the unpleasant trembling artifact is no longer evident.

• International Journal of Knowledge Content Development & Technology
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• 제7권1호
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

• 한국측량학회지
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• 제30권4호
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• pp.397-404
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• 2012

농업과 관광이 주요 산업인 제주지역은 소득 증대를 위해 노지재배에서 시설재배로의 전환이 활발하게 진행되고 있으므로 하우스재배지에 대한 지속적인 현황 파악이 필요하다. 이에 본 연구에서는 고해상도 위성영상을 이용하여 하우스재배지 탐지를 위한 효과적인 영상분류 방법을 제시하고자 하였다. Formosat-2 위성영상을 대상으로 감독분류와 규칙기반분류 방법을 적용하여 하우스재배지를 분류하였으며, 두 가지 결과를 연계하여 하우스재배지 탐지를 위한 정확도 향상 방안을 모색하였다. 각 분류 방법별 결과는 육안 탐지 결과와의 비교를 통해 정확도를 산출하였다. 연구 결과, 감독분류 방법 중 마하라노비스 거리법이 가장 높은 탐지 결과를 얻을 수 있었으며 감독분류 결과와 규칙기반분류 결과의 연계 시 탐지 정확도가 향상됨을 확인하였다. 향후 감독분류 결과와 규칙기반분류 결과의 연계 과정에 대한 추가적인 연구가 이루어진다면 하우스재배지의 효율적인 탐지가 가능할 것으로 기대된다.