• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.51 no.2
• /
• pp.171-178
• /
• 2015

Selectivity and by-catch are the main features used to define fish catch for fisheries management or to determine fishing gear efficiency. A fish girth distribution analysis was carried out to determine the retention rate of juveniles discarded and to establish common selectivity for a multispecies catch. Total body length, body weight, and girth of juvenile fish retained in the cod-end and cover net separately were collected using an 18-mm mesh cover net during 12 fishing trials with a 41-mm cod-end beam trawl. The by-catch weight ratio in the cod-end was twice that of the by-catch ratio in number. The 50% selectivity of body length and 50% girth of redfin velvet fish were not significantly different between the cod-end and cover net, whereas those of other fish species were significantly different between the cod-end and cover net. The difference in 50% selectivity girth of other fish between the cod-end and cover net was similar between cod-end mesh size and cover net mesh size. Furthermore, the difference in 50% body length selectivity of other fish between the cod-end and cover net was almost double the difference in girth. Girth selectivity in a multispecies catch using towed fishing gear was effectively used to determine fish and net mesh size.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.55 no.3
• /
• pp.181-189
• /
• 2019

Discarding juvenile fishes under girth 16 cm nearly equal to inner perimeter of codend mesh size collected by a cover net method in bottom trawl. The body size of the main five species (mackerel, horse mackerel, sea bream, melon seed and black throat seaperch) was measured for their body length, girth, weight, height and width and analyzed size selectivity. Frequency of penetrating fish as retention in a cover net was less than 40% of total number of juvenile discarding fish. The most of body length or girth of five species were significantly different between in the codend and in the cover net. The 50% selection girth in the cover net ranged 8-11 cm were smaller than those in the codend ranged 9-13 cm by the species respectively. The 50% selection body length was significantly related with the ratio of body height (H) by body width (W) both for in the codend or in the cover net while 50% selection girth was not significantly related with H/W. Furthermore 50% selection fish size by fish species between in the codend and in the cover net was not significantly different both in body length or girth. Therefore, the girth selectivity represented possibly as one unique value regarding fish body shape was considered as more useful method for multi-species catch in trawl.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.871-884
• /
• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Korean Journal of Remote Sensing
• /
• v.35 no.2
• /
• pp.279-288
• /
• 2019

We evaluated the land cover classification performance of SegNet, which features semantic segmentation of aerial imagery. We selected four semantic classes, i.e., urban, farmland, forest, and water areas, and created 2,000 datasets using aerial images and land cover maps. The datasets were divided at a 8:2 ratio into training (1,600) and validation datasets (400); we evaluated validation accuracy after tuning the hyperparameters. SegNet performance was optimal at a batch size of five with 100,000 iterations. When 200 test datasets were subjected to semantic segmentation using the trained SegNet model, the accuracies were farmland 87.89%, forest 87.18%, water 83.66%, and urban regions 82.67%; the overall accuracy was 85.48%. Thus, deep learning-based semantic segmentation can be used to classify land cover.

• Journal of IKEEE
• /
• v.27 no.4
• /
• pp.456-463
• /
• 2023

In this paper, we introduce the SSResUNet network model, which integrates the SPADE structure with the U-Net network model for accurate land cover classification using high-resolution satellite imagery without requiring user intervention. The proposed network possesses the advantage of preserving the spatial characteristics inherent in satellite imagery, rendering it a robust classification model even in intricate environments. Experimental results, obtained through training on KOMPSAT-3A satellite images, exhibit superior performance compared to conventional U-Net and U-Net++ models, showcasing an average Intersection over Union (IoU) of 76.10 and a Dice coefficient of 86.22.

• Korean Journal of Remote Sensing
• /
• v.39 no.3
• /
• pp.269-282
• /
• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• The Korean Journal of Ecology
• /
• v.18 no.2
• /
• pp.219-230
• /
• 1995

Although many studies of nutrient cycling in forest ecosystems have reported that clearcutting creates increased organic matter decomposition and nitrogen (N) mineralization in soils, little is known about the change of these factors following various levels of canopy removal. A series of experimental plots with four levels of canopy cover, i.e., clearcut, 25％, 75％, and uncut, was established in northern red oak (Quercus rubra L.) stands in northern Lover Michigan, U.S.A. I examined decomposition of cellulose filter papers and N mineralization using an in situ soil incubation technique in the top 15cm of mineral soil during the second growing season (1992, May-October) following stand manipulation. Mass loss from cellulose filter papers was more rapid in the canopy removal treatments than in the uncut treatment. similarly, net N mineralization was significantly greater in the canopy removal treatments than in the uncut treatment. There was no significant difference in net N mineralization rates among the three levels of canopy removal. Net N mineralization for the growing season was 58 kg/ha for the clearcut, 54 kg/ha for the 25％ canopy cover, 51 kg/ha for the 75％ canopy cover, and 22 kg/ha for the uncut treatment. These results indicated that even only small amounts of canopy removal (leaving 75％ canopy cover) let to substantial increases of cellulose decomposition and the amount of available soil nitrogen.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_2
• /
• pp.1591-1604
• /
• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.4
• /
• pp.63-80
• /
• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.5
• /
• pp.535-546
• /
• 2020

Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented.