• The Korean Journal of Malacology
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• v.6 no.1
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• pp.45-55
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• 1990

The present study on the classification and description of the marine mesogastropods based on the materials which were collected during the period from 12th to 17th of July in 1989 at nine localities of the Ullug Island. Seven unrecored species in 6 families of mesogastropodes are new to the fauna of Ullung Island. As a result of this study, 7 families and 8 species of marind mesogastuopoes are riported from the Ullung Island. Two species of thim, Costalynia costulata(Dunker, 1860), Barleeia angustata(Pilsbry, 1901), are found to be new to the fauna of Korea.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.540-547
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• 2011

This study was conducted to compare classification accuracy by tree species using QuickBird imagery for pixel- and segment-based classifications that have been mostly applied to classify land covers. A total of 398 points was used as training and reference data. Based on this points, the points were classified into fourteen land cover classes: four coniferous and seven deciduous tree species in forest classes, and three non-forested classes. In pixel-based classification, three images obtained by using raw spectral values, three tasseled indices, and three components from principal component analysis were produced. For the both classification processes, the maximum likelihood method was applied. In the pixel-based classification, it was resulted that the classification accuracy with raw spectral values was better than those by the other band combinations. As resulted that, the segment-based classification with a scale factor of 50% provided the most accurate classification (overall accuracy:76% and ${\hat{k}}$ value:0.74) compared to the other scale factors and pixel-based classification.

• Journal of the Korean Wood Science and Technology
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• v.47 no.4
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• pp.385-392
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• 2019

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

• Korean Journal of Pharmacognosy
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• v.43 no.4
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• pp.268-273
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• 2012

Acanthopanax species are well known medicinal plants in Korea for their adaptogenic efficacy. Regarding to the botanical classification of Acanthopanax koreanum, an indigenous species in Jeju island of Korea, it has been classified as different species of Acanthopanax genus. However, the morphological characteristics of A. koreanum are very similar with other Acanthopanax species, especially with A. gracilistylus. In order to provide further classification information among these botanically related species, microscopic and morphological studies on these Acanthopanax species were performed. In this result, it has been found that A. koreanum is similar to A. gracilistylus in terms of anatomical observation and was distinguished from A. sieboldianus by their morphological and anatomical differences.

• Journal of Microbiology and Biotechnology
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• v.11 no.3
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• pp.475-481
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• 2001

Sequences of ribosomal internal transcribed spaces (ITS) obtained from two Antrobia species and two related species were compared to investigate intrageneric and intergeneric phylogenetic relationships of Antrodia. The results showed that Antrodia species causing a brown rot in wood did not form a monophyletic clade and were separated into three distinct groups. Antrodia gossypina and A. vaillantii formed a clade having rhizomorphs as a homologous character. Antrodia serialis, A. sinuosa, and A. malicola formed a group together with Daedalea, Fomitopsis, and Postia species with brown rot habit. Antrodia xantha with a trimitic hyphal system and amyloid skeletal hyphae formed another distinct clade form other Antrodia species. The Antrodia species were separated from white rot genera such as Antrodiella, Diplomitoporus, Junghuhnia, and Steccherinum, indicating the phylogenetic importance of the rot type in the classification of the Polyporaceae.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.464-465
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• 2018

Bass and Bluegill, which are representative ecosystem disturbance species, are reported to be the most important factor in the reduction of domestic native fish populations in Korea. Therefore, it is necessary to develop system and field application technology for the extermination of these foreign species. Recently, the CNN(Convolutional Neural Network), one of the deep learning systems for the recognition, classification, and learning, has shown excellent performance. However, CNN data used for object recognition and classification were mainly applied to recognition and classification of other objects with distinct characteristics. This study proposes a system that applies CNN to the classification of fish species with similar characteristics.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.23-30
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• 2020

The use of increased computing power, machine learning, and deep learning techniques have dramatically increased in various sectors. In particular, image detection algorithms are broadly used in forestry and remote sensing areas to identify forest types and tree species. However, in South Korea, machine learning has rarely, if ever, been applied in forestry image detection, especially to classify tree species. This study integrates the application of machine learning and forest image detection; specifically, we compared the ability of two machine learning data collection methods, namely image data captured by forest experts (D1) and web-crawling (D2), to automate the classification of five trees species. In addition, two methods of characterization to train/test the system were investigated. The results indicated a significant difference in classification accuracy between D1 and D2: the classification accuracy of D1 was higher than that of D2. In order to increase the classification accuracy of D2, additional data filtering techniques were required to reduce the noise of uncensored image data.

• Journal of the Korean Wood Science and Technology
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• v.47 no.3
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• pp.265-276
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• 2019

In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of $128{\times}128{\times}3pixels$ via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Animal Systematics, Evolution and Diversity
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• v.31 no.3
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• pp.160-175
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• 2015

The echolocation calls of bats can provide useful information about species that are generally difficult to observe in the field. In many cases characteristics of call structure can be used to identify species and also to obtain information about aspects of the bat's ecology. We describe and compare the echolocation call structure of 14 of the 21 bat species found in Korea, for most of which the ecology and behavior are poorly understood. In total, 1,129 pulses were analyzed from 93 echolocation call sequences of 14 species. Analyzed pulses could be classified into three types according to the pulse shape: FM/CF/FM type, FM type and FM/QCF type. Pulse structures of all species were consistent with previous studies, although geographic variation may be indicated in some species. Overall classification rate provided by the canonical discriminant analysis was relatively low. Especially in the genera Myotis and Murina, there are large overlaps in spectral and temporal parameters between species. On the other hand, classification rates for the FM/QCF type species were relatively high. The results show that acoustic monitoring could be a powerful tool for assessing bat activity and distribution in Korea, at least for FM/QCF and FM/CF/FM species.