• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• 제어로봇시스템학회논문지
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• 제15권8호
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• pp.778-785
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• 2009

This paper presents a Gabor texture feature extraction method for classification of discolored Metal pad images using GPU(Graphics Processing Unit). The proposed algorithm extracts the texture information using Gabor filters and constructs a pattern map using the extracted information. Finally, the golden pad images are classified by utilizing the feature vectors which are extracted from the constructed pattern map. In order to evaluate the performance of the Gabor texture feature extraction algorithm based on GPU, a sequential processing and parallel processing using OpenMP in CPU of this algorithm were adopted. Also, the proposed algorithm was implemented by using Global memory and Shared memory in GPU. The experimental results were demonstrated that the method using Shared memory in GPU provides the best performance. For evaluating the effectiveness of extracted Gabor texture features, an experimental validation has been conducted on a database of 20 Metal pad images and the experiment has shown no mis-classification.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2468-2478
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• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.

• 한국측량학회지
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• 제38권5호
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• pp.435-441
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• 2020

Sentinel-2A satellite imagery provides VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) wavelength bands, and it is known to be effective for land cover classification, cloud detection, and environmental monitoring. Greenhouse is one of the middle classification classes for land cover map provided by the Ministry of Environment of the Republic of Korea. Since greenhouse is a class that has a lot of changes due to natural disasters such as storm and flood damage, there is a limit to updating the greenhouse at a rapid cycle in the land cover map. In the present study, we utilized Sentinel-2A satellite images that provide both VNIR and SWIR bands for the detection of greenhouse. To utilize Sentinel-2A satellite images for the detection of greenhouse, we produced high-resolution SWIR bands applying to the fusion technique performed in two stages and carried out the detection of greenhouse using SVM (Support Vector Machine) supervised classification technique. In order to analyze the applicability of SWIR bands to greenhouse detection, comparative evaluation was performed using the detection results applying only VNIR bands. As a results of quantitative and qualitative evaluation, the result of detection by additionally applying SWIR bands was found to be superior to the result of applying only VNIR bands.

• 정보보호학회논문지
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• 제29권1호
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• pp.117-125
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• 2019

최근 다양한 분야에서 활용중인 딥러닝은 적대적 공격 가능성의 발견으로 위험성이 제기되고 있다. 본 논문에서는 딥러닝의 이미지 분류 모델에서 악의적 공격자가 생성한 적대적 샘플에 의해 분류 정확도가 낮아짐을 실험적으로 검증하였다. 대표적인 이미지 샘플인 MNIST데이터 셋을 사용하였으며, 텐서플로우와 파이토치라이브러리를 사용하여 만든 오토인코더 분류 모델과 CNN(Convolution neural network)분류 모델에 적대적 샘플을 주입하여 탐지 정확도를 측정한다. 적대적 샘플은 MNIST테스트 데이터 셋을 JSMA(Jacobian-based Saliency Map Attack)방법으로 생성한 방법과 FGSM(Fast Gradient Sign Method)방식으로 변형하여 생성하였으며, 분류 모델에 주입하여 측정하였을 때 최소 21.82%에서 최대 39.08%만큼 탐지 정확도가 낮아짐을 검증하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.439-442
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• 1999

It is important to analyze the seasonal change profiles of land cover type in large scale for establishing preservation strategy and environmental monitoring. Because the NOAA-AVHRR data sets provide global data with high temporal resolution, it is suitable for the land cover classification of the large area. The objectives of this study were to classify land cover of Korea, to investigate the phenological profiles of land cover. The NOAA-AVHRR data from Jan. 1998 to Dec. 1998 were received by Korea Ocean Research & Development Institute(KORDI) and were used for this study. The NDVI data were produced from this data. And monthly maximum value composite data were made for reducing cloud effect and temporal classification. And the data were classified using the method of supervised classification. To label the land cover classes, they were classified again using generalized vegetation map and Landsat-TM classified image. And the profiles of each class was analyzed according to each month. Results of this study can be summarized as follows. First, it was verified that the use of vegetation map and TM classified map was available to obtain the temporal class labeling with NOAA-AVHRR. Second, phenological characteristics of plant communities of Korea using NOAA-AVHRR was identified. Third, NDVI of North Korea is lower on Summer than that of South Korea. And finally, Forest cover is higher than another cover types. Broadleaf forest is highest on may. Outline of covertype profiles was investigated.

• 한국정보과학회논문지:소프트웨어및응용
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• 제28권12호
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• pp.875-884
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• 2001

자기 구성지도는 데이타 시각화, 위상보존 매핑 등의 분야에서 널리 사용되고 있지만. 학습이 되기 전에 위상을 미리 고정시켜야 하기 때문에 실제 문제에 적용하기 어렵다는 것과 클러스터링 능력에 비해 분류율이 낮다는 결점이 있다. 이를 해결하기 위해서 자기구성 지도의 출력 노드를 동적으로 분화하고 분화된 노드를 파습하는 동적 위상보존 사기구성 지도를 제안하고, 이를 다중 결합함으로써 분류율을 향상 시켰다. 동적 위상보존 자기구성 지도의 결함 방법으로는 자기구성 지도의 K개 노드가 출격을 내도록하는 K-Winner 방법 및 K-Winner+ 가중치 방법이 제안되었는데, 이는 다수결 투표, 가중치, BKS, Byayesian, Borda, Condorect, 신뢰값 합산 등의 기존 결합 방법보다도 우수한 결과를 나타내었다. 동적 위상보존 자기 구성 지도를 통해서 위상을 고정 시켜야 하는 결점을 해결할 수 있었고. 서로 다른 특징으로 학습된 동적 위상보존 자기구성 지도들을 결합하여 분류 능력을 향상시킬 수 있었다. 필기 숫자데이타로. 실험한 결과, 제안한 방법이 자기구성 지도의 결점을 효과적으로 해결하여 98.1% 의 높은 인식률을 보였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.221-224
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• 2005

Hyperspectral image data have great potential to depict more detailed information on biophysical characteristics of surface materials, which are not usually available with multispectral data. This study aims to test the potential of hyperspectral data for classifying five soil classes defined by the vector product interim terrain data (VITD). In this study, we try to classify surface materials of bare soil over the study area in Korea using both hyperspectral and multispectral image data. Training and test samples for classification are selected with using VITD vector map. The spectral angle mapper (SAM) method is applied to the EO-I Hyperion data and Landsat ETM+ data, that has been radiometrically corrected and geo-rectified. Higher classification accuracy is obtained with the hyperspectral data for classifying five soil classes of gravel, evaporites, inorganic silt and sand.

• 대한지구과학교육학회지
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• 제3권1호
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• pp.47-54
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• 2010

The purpose of this study is finding examine the Thinking Maps and how to use Thinking Maps effectively in Science Education. The result of this study were as follows: First, There are 8 type Maps, Circle Map, Tree Maps, Bubble Map, Double Bubble Map, Flow Map, Multi Flow Map, Brace Map, Bridge Map. Each Maps are useful in the following activities ; Circle Map-Express their thoughts. Tree Map-Activities as like determine the structure, classification, information organization. Bubble Maps-Construction. Double Bubble Map-Comparison of similarities and differences. Flow Map-Set goals, determine the result of changes in time or place. Multi Flow Map-Analysis cause and effect, expectation and reasoning. Brace Map-Analysis whole and part. Bridge Map-Activities need analogies. Second, each element of inquiry has 1~2 appropriate type of Thinking Maps. So student can choose the desired map. Third, the result of analysing of Science Curriculum Subjects, depending on the subject variety maps can be used. Therefore the Thinking Maps can be used for a variety on activities and subject. And student can be selected according to their learning style. So Thinking Maps are effective to improve student's Self-Directed Learning.

• 한국지리정보학회지
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• 제20권1호
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• pp.1-14
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• 2017

토지피복도는 지역의 현황을 파악하는 기초적 자료이지만 시간적 공간적 해상도의 한계로 인하여 생태 연구 분야에서의 활용성은 떨어지는 측면이 있다. 이에 본 연구에서는 UAV으로 취득된 고해상도 영상을 기반으로 토지피복도 제작과 자료의 활용가능성을 알아보고자 하였다. UAV를 이용하여 연구대상지 $2.5km^2$ 범위에서 10.5cm 정사영상을 취득하였으며 객체기반(Object-based)과 화소기반(pixel-based) 분류를 통해 얻어진 토지피복도를 비교 분석하였다. 정확도 검증 결과 화소기반 분류는 Kappa 0.77, 객체기반 분류는 Kappa 0.82로 분류정확도가 높았으며, 전반적인 면적비율은 유사하지만 초지, 습지 지역에서 양호한 분류 결과가 나타났다. 객체기반 분류를 위한 최적의 영상분할 가중치는 Scale150, Shape 0.5, Compactness 0.5, Color 1로 선정하였으며 가중치 선정과정에서 Scale이 가장 큰 영향을 주었다. 화소기반 분류 결과와 비교해 객체간의 명확한 경계를 가지므로 결과물 판독이 용이한 것으로 나타났으며, 환경부 토지피복도(세분류)와 비교하여 개발지역(도로, 건물 등)을 제외한 자연지역(산림, 초지, 습지 등)의 분류에 효과적이었다. UAV 영상을 활용한 토지피복 분류방법으로서 객체기반 분류기법의 적용은 자료의 최신성, 정확성, 경제성 등의 장점으로 생태 연구 분야에 기여할 수 있을 것으로 판단된다.