• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.184-189
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• 2004

A soil moisture measuring method for a hillslope of Korean watershed was developed to configure spatial-temporal distribution of soil moisture. Intensive surveying of topography had been performed to make a refined digital elevation model(DEM) and the hydrological interpretation from flow distribution algorithm was incorporated through reverse surveying. Moreover, A long term measurement system was established to maximize representative features of spatial variation of soil moisture and operated from October 19 to 21, 2003. TDR(Time Domain Reflectometry) with a multiplex monitoring system has been operated for accurate measurements. Measurements were performed at the right side hillslope of Buprunsa located at the sulmachun watershed. The data of temporal and spatial soil moisture variation by rainfall event were collected and the variations of soil moisture were well captured.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.927-939
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• 2020

Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.553-556
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• 2004

Spatial and temporal aspects of toxic dinoflagellate Cochlodinium.p blooms and consequences of physical features in complex coastal ecosystems, off the southern Korean coast, have been investigated using data obtained from SeaWiFS and AVHRR as well as in-situ observations. Hydrographic parameters measured using CTD sensors were used to elucidate physical factors affecting the spatial distribution and abundance of Cochlodinium.p blooms. The results show spatial and temporal variations of chlorophyll-a (Chl-a) and sea surface temperature (SST) and reveal significant information about Cochlodinium.p blooms and process underlying their evolution. Satellitederived Chl-a estimates appear to be potential in explicating the evolution, movement and distribution of Cochlodinium.p blooms in the enclosed bays of the South Sea. The existence of thromohaline waters offshore provide favorable conditions for the rapid growth and subsequent southward initiation of Cochlodinium.p blooms that are influenced to flow on the offshore branch (OB) during September. It was observed that there was a significant variation in the sun-induced chlorophyll-a fluorescence signal in the remote sensing fluorescence spectra and its high-intensity was recognized during the period of exponential growth and physical transport. Satellite-derived Chl-a concentration during September 1999 ranged between $3­60mg/m^3$ inside the Jin-hae and adjacent Bays and $1-6mg/m^3$ in offshore waters, with varying Cochlodinium.p abundances 1500 to 26000 cells $ml^{-1}.$ The closely spaced CTD surveys and satellite-derived SST give a complete overview on the initiation of Cochlodinium.p blooms in hydrodynamically active regions of the offshore southern East Sea by the influence of Tsushima Warm Current (TWC).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.799-808
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• 2016

The IEEE 802.15.7r1 Task Group (TG7r1), known as the revision of the IEEE 802.15.7 Visible Light Communication standard targeting the commercial usage of visible light communication systems which mainly use either image sensors or cameras, is of interest in this paper. The vast challenge in Image Sensor Communications (ISC), as it has been addressed in the Technical Consideration Document (TCD) of the TG7r1, is the Image Sensor Compatibility to support the variety of different commercial cameras available on the market. The on-going ISC standard must adhere to compatible image sensors regulations. This paper brings an inside review of the TG7r1 and an inside look of related works on Image Sensor Communications. The paper analyzes the compatibility features by introducing a revised model of receiver to explain how those features are necessary. One of the most challenging but interesting features is the capability in being compatible to camera frame rates. The variation of camera frame rate is modeled from verified experimental results. Noticeably, three singular approaches to support frame rates compatibility, including temporal approach, spatial approach, and frequency-domain approach, are proposed on the paper along with concise definitions. Those schemes have been presented as valuable proposals on the call-for-proposal meeting series of the TG7r1 recently.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.151-152
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• 2008

The planning and management of the watershed environment require huge amount of information regarding almost all aspects of natural and manmade features of the area. Until lately this study could be achieved through days of exhaustive surveys map generation and tedious calculations. Remote sensing and GIS provides huge temporal database for an area and GIS provides the powerful tool for spatial and non-spatial analysis of remotely sensed data. The paper highlights the assessment of land potentiality using weighed overlay analysis with drainage density, soil, slope and lineament, LULC map was used to identify the utilization area of the watershed. The arithmetic overlay analysis was performed with potential and utilization layer to assess the availability of land for the future development.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.42-51
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• 2008

Robots need to understand as much as possible about their environmental situation and react appropriately to any event that provokes changes in their behavior. In this paper, we pay attention to topological relations between spatial objects and propose a model of robotic cognition that represents and infers temporal relations. Specifically, the proposed model extracts specified features of the cooccurrence matrix represents from disparity images of the stereo vision system. More importantly, a habituation model is used to infer intrinsic spatial relations between objects. A preliminary experimental investigation is carried out to verify the validity of the proposed method under real test condition.

• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.