• Proceedings of the KSRS Conference
• /
• 2008.03a
• /
• pp.219-223
• /
• 2008

초분광 영상은 기존의 다중분광 영상보다 많은 파장대의 영상을 취득하기 때문에 다양한 분야의 연구에 이용되고 있다. 하지만 밴드별로 취득하는 파장대가 짧고 밴드수가 많아, 밴드간의 높은 상관관계 및 노이즈 밴드가 존재한다. 이로 인해 기존에 알려진 분석기법의 적용결과가 제대로 도출되지 않는다. 따라서 초분광 영상을 이용할 경우, 노이즈가 많이 포함된 밴드를 제거한 후 영상분석을 하는 것이 보다 효율적이다. 본 연구에서는 초분광 영상(Hyperspectral Image)의 전처리 과정 중 노이즈 밴드 제거에 초점을 맞추었으며, 이를 위해 프랙탈 차원을 이용하였다. 프랙탈 차원 측정방법 중 삼각기둥 표면적 기법을 이용하였다. 프랙탈 차원을 측정하고, Continuum Removal 기법을 이용하여 경향을 살펴보았다. 경험적으로 구한 임계값을 통해 상대적으로 정보량이 적은 밴드를 노이즈 밴드로 판단하여 제거하였다. 실험 영상으로는 EO-1 위성에서 취득되는 Hyperion 초분광 영상을 사용하였다. 실험 결과 프랙탈 분석을 통해 Hyperion 초분광 영상의 노이즈 밴드를 자동으로 추출하여 제거할 수 있음을 확인하였다.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.576-578
• /
• 2003

Hyperspectral image divides VNIR region to over 200 bands which can show continuous spectrum with 10 nm spectral resolution. This property is useful in geology where a spectral feature which is decided by chemical compositions and crystalline structures is recorded well. While this field has been studied variously in foreign countries, the studies are in the early stage in Korea. In this study, characteristic materials associated with AMD were classified by using EO-1 HYPERION data which is a spaceborne hyperspectral image and topographical map and DEM and geochemical map were analyzed in conjunction with the image in order to examine that classified minerals are secondary minerals by AMD.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.134-137
• /
• 2005

Unsupervised spectral angle classification (USAC) is the algorithm that can extract ground object information with the minimum 'Spectral Angle' operation on behalf of 'Spectral Euclidian Distance' in the clustering process. In this study, our algorithm uses the unit vector instead of the spectral distance to compute the mean of cluster in the unsupervised classification. The proposed algorithm (MUSAC) is applied to the Hyperion and ETM+ data and the results are compared with K-Meails and former USAC algorithm (FUSAC). USAC is capable of clearly classifying water and dark forest area and produces more accurate results than K-Means. Atmospheric correction for more accurate results was adapted on the Hyperion data (Hyperion-FLAASH) but the results did not have any effect on the accuracy. Thus we anticipate that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but also hyperspectral images. Furthermore the cluster unit vector can be an efficient technique for determination of each cluster mean in the USAC.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.383-387
• /
• 2007

The improved accuracy of the Land Cover/Land Use Map constructed using Hyperspectal Satellite Image and the possibility of real time classification of Land Use using optimal Band Selective Factor enable the change detection from automatic classification using the existed Land Cover/Land Use Map and the newly acquired Hyperspectral Satellite Image. In this study, the effective analysis techniques for automatic generation of training regions, automatic classification and automatic change detection are proposed to minimize the expert's interpretation for automatic update of the Land Cover/Land Use Map. The proposed algorithms performed successfully the automatic Land Cover/Land Use Map construction, automatic change detection and automatic update on the image which contained the changed region. It would increase applicability in actual services. Also, it would be expected to present the effective methods of constructing national land monitoring system.

• Korean Journal of Geomatics
• /
• v.5 no.1
• /
• pp.27-34
• /
• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.221-224
• /
• 2005

Hyperspectral image data have great potential to depict more detailed information on biophysical characteristics of surface materials, which are not usually available with multispectral data. This study aims to test the potential of hyperspectral data for classifying five soil classes defined by the vector product interim terrain data (VITD). In this study, we try to classify surface materials of bare soil over the study area in Korea using both hyperspectral and multispectral image data. Training and test samples for classification are selected with using VITD vector map. The spectral angle mapper (SAM) method is applied to the EO-I Hyperion data and Landsat ETM+ data, that has been radiometrically corrected and geo-rectified. Higher classification accuracy is obtained with the hyperspectral data for classifying five soil classes of gravel, evaporites, inorganic silt and sand.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2007.06a
• /
• pp.139-144
• /
• 2007

Recording data in hundreds of narrow contiguous spectral intervals, hyperspectral images have provided the opportunity to detect small differences in material composition. But a limitation of a hyperspectral image is the signal to noise ratio (SNR) lower than that of a multispectral image. This paper presents the efficiency of Spectral Similarity Scale (SSS) in change detection of hyperspectral image and the experiment was performed with Hyperion data. SSS is an algorithm that objectively quantifies differences between reflectance spectra in both magnitude and direction dimensions. The thresholds for detecting the change area were determined through Expectation-Maximization (EM) algorithm. The experimental result shows that the SSS algorithm and EM algorithm are efficient enough to be applied to the unsupervised change detection of hyperspectral images.

• Korean Journal of Remote Sensing
• /
• v.22 no.3
• /
• pp.175-182
• /
• 2006

Because of the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions of agricultural croplands, grasslands, and disturbed forests by multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been a primary problem to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement (GER-2600) and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Red-edge near 700nm and shortwave IR near 2,200nm are more sensitive to the fraction of dry vegetation. The use of hyperspectral data would allow us for better separation between bare soils and other surfaces covered by dry vegetation during the leaf-off season.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.26 no.2
• /
• pp.129-137
• /
• 2008

This study focuses on the radiometric normalization, which is one of the pre-processing steps to apply the change detection technique fur hyperspectral images. The PIFs which had radiometric consistency under the time interval were automatically extracted by applying spectral angle, and used as sample pixels for linear regression of the radiometric normalization. We also dealt with the problem about the number of PIFs for linear regression with iteratively quantitative methods. The results were assessed in comparison with image regression, histogram matching, and FLAASH. In conclusion, we show that linear regression method with PIFs can carry out the efficient result for radiometric normalization.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.04a
• /
• pp.159-164
• /
• 2004

SAM(Spectral Angle Mapper) is the method using the similarly of the angle between pairs of signatures instead of the spectral distance(MDC, MLC etc.) for classification or clustering. In this paper, we applied unsupervised techniques(Unsupervised SAM and ISODATA) to the Hyperspectral Image(Hyperion) which has innumerable, narrow and contiguous spectral bands and Multispectral Image(ETM$\^$+/) for the clustering of signatures. The overall measured accuracies of the USAM and ISODATA of multispectral image were 76.52%, 53.91% and the USAM and ISODATA of hyperspectral image were 63.04%, 53.91%. From the results of our test, we report that the Unsupervised SAM is better classfication technique than ISODATA. Also we believe that the "Spectral Angle" can potentially be one of the most accurate classifier not only multispectral images but hyperspectral images.