• Korean Journal of Remote Sensing
• /
• v.21 no.4
• /
• pp.341-369
• /
• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Korean Journal of Remote Sensing
• /
• v.8 no.2
• /
• pp.125-130
• /
• 1992

Interconnecting remote sensing systems to geographic information systems is valuable in many different applications. Two common techniques for moving data between these two related kinds of spatial data-processing systems were discussed. Digital classification of remote sensing data for use in natural resource inventory has produced mixed results. In attempts to improve classification, accuracy ancillary data, such as digitized maps and terrain(elevation) data, have been combined with remotely sensed data in various ways. These data have been used commonly in (1) preclassification scene stratification and (2) postclassification class sorting. These two approaches are found to be efficient, but lacking in sophistication due to their reliance on deterministic decision rules.

• Korean Journal of Remote Sensing
• /
• v.28 no.1
• /
• pp.29-38
• /
• 2012

Nowadays, most remote sensing image processing systems are on client-based ones. But in the view of information technology, a web-based system is predominant, being closely related to cloud computing and services. The web-based system in remote sensing is somewhat limited in the area of data sharing or dissemination, but it is necessary to extend. This study is to implement a web-based system and its component modules for SAR data processing. First, the previous cases dealt with both web computing and SAR information are investigated. InSAR information processing and concerned modules for a web-based system among SAR research domains are the main points in this work. It is expected that this approach contributes to the first attempt to link web computing technology such as HTML5 and satellite image processing.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.661-667
• /
• 2022

As remote sensing and GIS have been considered to be essential technologies for disasters information production, researches on developing methods for analyzing spatial data, and developing new technologies for such purposes, have been actively conducted. Especially, it is assumed that the use of remote sensing and GIS for disaster management will continue to develop thanks to the launch of recent satellite constellations, the use of various remote sensing platforms, the improvement of acquired data processing and storage capacity, and the advancement of artificial intelligence technology. This spatial issue presents 10 research papers regarding ship detection, building information extraction, ocean environment monitoring, flood monitoring, forest fire detection, and decision making using remote sensing and GIS technologies, which can be applied at the disaster prediction, monitoring and response stages. It is anticipated that the papers published in this special issue could be a valuable reference for developing technologies for disaster management and academic advancement of related fields.

• Korean Journal of Remote Sensing
• /
• v.31 no.6
• /
• pp.513-521
• /
• 2015

In the processing of ocean color remote sensing data, spatio-temporal binning is crucial for securing effective observation area. The validity determination for given source data refers to the information in Level-2 flag. For minimizing the stray light contamination, NASA OBPG's standard algorithm suggests the use of large filtering window but it results in the loss of effective observation area. This study is aimed for quality improvement of ocean color remote sensing data by recovering/extending the portion of effective observation area. We analyzed the difference between MODIS/Aqua standard and modified product in terms of chlorophyll-a concentration, spatial and temporal coverage. The recovery fractions in Level-2 swath product, Level-3 daily composite product, 8-day composite product, and monthly composite product were $13.2({\pm}5.2)%$, $30.8({\pm}16.3)%$, $15.8({\pm}9.2)%$, and $6.0({\pm}5.6)%$, respectively. The mean difference between chlorophyll-a concentrations of two products was only 0.012%, which is smaller than the nominal precision of the geophysical parameter estimation. Increase in areal coverage also results in the increase in temporal density of multi-temporal dataset, and this processing gain was most effective in 8-day composite data. The proposed method can contribute for the quality enhancement of ocean color remote sensing data by improving not only the data productivity but also statistical stability from increased number of samples.

• Journal of the Korean earth science society
• /
• v.27 no.1
• /
• pp.83-94
• /
• 2006

The objective of this paper is to extract and analyze the sediment environment change information in the Sachencheon, Gangneung, Korea that was seriously damaged as a result of typhoon Rusa aftermath early in September, 2002 using multi-temporal remote sensing data. For the extraction of change information, an unsupervised approach based on the automatic determination of thresholding values was applied. As the change detection results, turbidity changes right after typhoon Rusa, the decrease of wetlands, the increase of dry sand and channel width and changes of relative level in the stream due to seasonal variation were observed. Sedimentation in the cultivated areas and restoration works also affected the change near the Sacheoncheon. In addition to the change detection analysis, several environmental thematic maps including microtopographic map, distributions of estimated amount of flood deposits and flood hazard landform classification map were generated by using remote sensing and field survey data. In conclusion, multi-temporal remote sensing data can be effectively used for natural hazard analysis and damage information extraction and specific data processing techniques for high-resolution remote sensing data should also be developed.

• Korean Journal of Remote Sensing
• /
• v.7 no.2
• /
• pp.131-147
• /
• 1991

Feature enhancement combined with some pattern recognition techiques were applied to the Remote Sensing Data for geological mapping with particular emphasis on non-me-tallic ore deposits and their related geologies. The area chosen is north of Ulsan, the size of which is about 400km$^2$. The geology of the area consists mainly of volcanics, volcanic sediments and clastic sediments of Miocene age, underlain by the Kyungsang sediments of Cretaceous age. The mineralization occurs in tuffs or along the bedding plane of tuffaceous sediments, the main products of which are Kaolinite and Bentonite. The outcrops or mine dumps in the study area were most effectively extracted on the histrogram normalized image of TM Band 1 and 2, due to their high reflectivity. These may be confused with some artificial features, like slate roof complex of the poultry farm or cement ground, which should be classified by field checking. Detailed examination of enhancment image combined with pattern recognition techniques made enable to classify different rocks and thereby extract volcanic products which are mainly related to non-metallic ore deposits in the study area.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.9 no.4
• /
• pp.129-141
• /
• 2006

The image data amount is increasing rapidly that used geography, sea information etc. with great development of a remote sensing technology using artificial satellite. Therefore, people need automatic method that use image processing description than macrography for analysis remote sensing image. In this paper, we propose that acquire texture information to use GLCM(Gray Level Co-occurrence Matrix) in red tide area of artificial satellite remote sensing image, and detects red tide area by PCA(principal component analysis) automatically from this data. Method by sea color that one feature of remote sensing image of existent red tide area detection was most. but in this paper, we changed into 2 principal component accumulation images using GLCM's texture feature information 8. Experiment result, 2 principal component accumulation image's variance percentage is 90.4%. We compared with red tide area that use only sea color and It is better result.

• Aerospace Engineering and Technology
• /
• v.3 no.2
• /
• pp.141-148
• /
• 2004

The importance of satellite data for numerous applications is stressed by the fact that many countries have given the development of space technologies very high priority. Among these, Korea has established a medium-term space development strategy to promote space development both on a scientific as well as commercial level. As part of this strategy, the first operational earth-observation, multi-purpose satellite(KOMPSAT-1) was launched successfully in December, 1999. The Electro-Optical Camera (EOC) on board of KOMPSAT-1 supplies panchromatic images with a spatial resolution of 6.6m Until April, 2004, it collected over 150.000 images of the Korean Peninsula and the rest of the world. This paper examines the use of remote sensing data to analyze urban growth in the city of Daejeon from 1960 to 2003. By using visual interpretation, land use maps are created.

• Korean Journal of Remote Sensing
• /
• v.15 no.2
• /
• pp.147-158
• /
• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.