• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.227-237
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• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.404-417
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• 2017

In this study, we analyzed response patterns of rotifer community to eutrophic state, and estimated the applicability of rotifer community as an environmental indicator for highly eutrophicated reservoirs. In order to evaluate the relationships among spatial and temporal distributions and the water quality of rotifer community, we selected the Jundae Reservoir and Chodae Reservoir in Chungcheongnam-do, Korea, which are geographically adjacent but have different water quality, particularly in their eutrophic states. For the analyses on their correlations, monthly survey of water quality and rotifer community, was conducted from April to November 2013 in both reservoirs. The rotifer community was divided into different compositions of functional groups as well as species. Functional groups were classified according to the structure and shape of trophi which can represent feeding behavior of rotifer genus. To reflect ecological characteristics of species, body size and habitat preferences were also considered. Species-based composition did not show a consistent tendency with water quality parameters related with eutrophication. On the contrary, functional group composition showed relatively clear group-specific patterns, increasing or decreasing according to the parameters. The results suggest the possible application of rotifer functional group composition as an indicatorforthe lentic systems, especially hyper-eutrophicated reservoirs. The present study can suggest the applicability based on the field observations from the limited time scale and sites, and further studies on feeding behavior of the rotifer functional group and its interactions with environmental variables are necessary for the further application.