• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.419-422
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• 2004

This paper was carried out desertification area change detection from 1980s to 2000s per unit decade using by multitemporal satellite images (Landsat MSS, TM, ETM+). This study aims to use Spectral Mixture Analysis (SMA) to identify and classify study area. Endmembers is selected bare soil, green vegetation (GV), water body using by Minimum Noise Fraction (MNF). Endmembers used to generate increase and decrease images respective from 1980s to 1990s and from 1990s to 2000s. From the analysis of multitemporal change detection for three periods, it was apparent that the area of bare soil increased significantly, with simultaneous decrease of GV and water body. The multitemporal fraction images can be effectively used for change detection. Though there is no field survey dataset, SMA is reliable result of change detection in desertification in China.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.569-577
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• 2008

The purpose of this study is focused on the development of compound classification process by mixing multitemporal data and annexing a specific image enhancement technique with a specific image classification algorithm, to gain more accurate land information from satellite imagery. That is, this study suggests the classification process using canonical correlation classification technique after principal component analysis for the mixed multitemporal data. The result of this proposed classification process is compared with the canonical correlation classification result of one date images, multitemporal imagery and a mixed image after principal component analysis for one date images. The satellite images which are used are the Landsat 5 TM images acquired on July 26, 1994 and September 1, 1996. Ground truth data for accuracy assessment is obtained from topographic map and aerial photograph, and all of the study area is used for accuracy assessment. The proposed compound classification process showed superior efficiency to appling canonical correlation classification technique for only one date image in classification accuracy by 8.2%. Especially, it was valid in classifying mixed urban area correctly. Conclusively, to improve the classification accuracy when extracting land cover information using Landsat TM image, appling canonical correlation classification technique after principal component analysis for multitemporal imagery is very useful.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.427-435
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• 2000

It is often crucial to obtain a map of flood inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in Imjin river basin. Multitemporal RADARSAT SAR data of three different dates were obtained at the time of flooding on August 4 and before and after the flooding. Once the data sets were geometrically corrected and preprocessed, the temporal characteristics of relative radar backscattering were analyzed. By comparing the radar backscattering of several surface features, it was clear that the flooded rice paddy showed the distinctive temporal pattern of radar response. Flooded rice paddy showed significantly lower radar signal while the normally growing rice paddy show high radar returns, which also could be easily interpreted from the color composite imagery. In addition to delineating the flooded rice fields, the multitemporal radar imagery also allow us to distinguish the afterward condition of once-flooded rice field.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.515-525
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• 2020

This study investigates the potential of bidirectional long short-term memory (Bi-LSTM) for efficient modeling of temporal information in crop classification using multitemporal remote sensing images. Unlike unidirectional LSTM models that consider only either forward or backward states, Bi-LSTM could account for temporal dependency of time-series images in both forward and backward directions. This property of Bi-LSTM can be effectively applied to crop classification when it is difficult to obtain full time-series images covering the entire growth cycle of crops. The classification performance of the Bi-LSTM is compared with that of two unidirectional LSTM architectures (forward and backward) with respect to different input image combinations via a case study of crop classification in Anbadegi, Korea. When full time-series images were used as inputs for classification, the Bi-LSTM outperformed the other unidirectional LSTM architectures; however, the difference in classification accuracy from unidirectional LSTM was not substantial. On the contrary, when using multitemporal images that did not include useful information for the discrimination of crops, the Bi-LSTM could compensate for the information deficiency by including temporal information from both forward and backward states, thereby achieving the best classification accuracy, compared with the unidirectional LSTM. These case study results indicate the efficiency of the Bi-LSTM for crop classification, particularly when limited input images are available.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.327-338
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• 2000

The forest conditions of North Korea has been a great concern since it was known to be closely related to many environmental problems of the disastrous flooding, soil erosion, and food shortage. To assess the long-term changes of forest area as well as the canopy conditions, several sources of multitemporal satellite data were applied to the study area near Kaesung. KOMPSAT-1 EOC data were overlaid with 1981 topographic map showing the boundaries of forest to assess the deforestation area. Delineation of the cleared forest was performed by both visual interpretation and unsupervised classification. For analyzing the change of forest canopy condition, multiple scenes of Landsat and SPOT data were selected. After preprocessing of the multitemporal satellite data, such as image registration and normalization, the normalized difference vegetation index (NDVI) was derived as a representation of forest canopy conditions. Although the panchromatic EOC data had radiometric limitation to classify diverse cover types, they can be effectively used t detect and delineate the deforested area. The results showed that a large portion of forest land has been cleared for the urban and agricultural uses during the last twenty years. It was also found that the canopy condition of remaining forests has not been improved for the last twenty years. It was also found that the canopy condition of remaining forests has not been improved for the last twenty years. Possible causes of the deforestation and the temporal pattern of canopy conditions are discussed.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1233-1242
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• 2017

In this study, we tried to analyze deforestation due to forest fire by using KOMPSAT satellite imagery. For deforestation analysis, unsupervised change detection algorithm is applied to multitemporal images. Through ITPCA (ITerative Principal Component Analysis) of NDVI (Normalized Difference Vegetation Index) generated from multitemporal satellite images before and after forest fire, changed areas due to deforestation are extracted. In addition, a post-processing method using SRTM (Shuttle Radar Topographic Mission) data is involved in order to minimize the error of change detection. As a result of the experiment using KOMPSAT-2 and 3 images, it was confirmed that changed areas due to deforestation can be efficiently extracted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.51-58
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• 2018

Recently, various satellite sensors have been developed and it is becoming more convenient to acquire multitemporal satellite images. Therefore, various researches are being actively carried out in the field of utilizing change detection techniques such as disaster and land monitoring using multitemporal satellite images. In particular, researches related to the development of unsupervised change detection techniques capable of extracting rapidly change regions have been conducted. However, there is a disadvantage that false detection occurs due to a spectral difference such as a seasonal change. In order to overcome the disadvantages, this study aimed to reduce the false alarm detection due to seasonal effects using the direction vector generated by applying the $S^2CVA$ (Sequential Spectral Change Vector Analysis) technique, which is one of the unsupervised change detection methods. $S^2CVA$ technique was applied to RapidEye images of the same and different seasons. We analyzed whether the change direction vector of $S^2CVA$ can remove false positives due to seasonal effects. For the quantitative evaluation, the ROC (Receiver Operating Characteristic) curve and the AUC (Area Under Curve) value were calculated for the change detection results and it was confirmed that the change detection performance was improved compared with the change detection method using only the change magnitude vector.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.405-407
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• 2003

An attempt has been in this study to delineate the characteristics of spectral signatures of the vegetation in terms of various VIs, particularly made the Normalized Difference Vegetation Index(NDVI), Modified Soil Adjusted Vegetation Index2(MSAVI2) and Enhanced Vegetation Index(EVI). Multitemporal SPOT-4 VEGETATION data from 1998 to 2002 have been used for the analysis. They have been compared with each other for their similarities and differences. The correlations between the vegetation indices observed at various degree of vegetation coverage during their different stages of growth were examined. All of the VIs have shown qualitative relationships to variations in vegetation. Apparently, the NDVI and MSAVI2 are highly correlated for all of the temporal changes, representing the different stages of phenology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.24-33
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• 2017

Multitemporal MODIS vegetation index (VI) data are widely used in vegetation monitoring research into environmental and climate change, since they provide a profile of vegetation activity. However, MODIS data inevitably contain disturbances caused by the presence of clouds, atmospheric variability, and instrument problems, which impede the analysis of the NDVI time series data and limit its application utility. For this reason, preprocessing to reduce the noise and reconstruct high-quality temporal data streams is required for VI analysis. In this study, a data reconstruction method for MODIS NDVI is proposed to restore bad or missing data based on the statistical properties of the oscillations in the NDVI temporal dynamics. The first derivatives enable us to examine the monotonic properties of a function in the data stream and to detect anomalous changes, such as sudden spikes and drops. In this approach, only noisy data are corrected, while the other data are left intact to preserve the detailed temporal dynamics for further VI analysis. The proposed method was successfully tested and evaluated with simulated data and NDVI time series data covering Baekdu Mountain, located in the northern part of North Korea, over the period of interest from 2006 to 2012. The results show that it can be effectively employed as a preprocessing method for data reconstruction in MODIS NDVI analysis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.343-348
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• 1998

In this study, we extracted several terrain information using satellite and aerial images. We detected change of terrain using Landsat Thematic Mapper(TM) and aerial images which are multitemporal data. In change detection processing, we first classified satellite images by ISODATA algorithm which is an unsupervised learning algorithm, then performed change detection. By this method, we could obtain good result. Also we introduce sub-pixel concept to classify road and agriculture area in inaccessible area. In summary, in chang detection processing, we can find that the used method is efficient.