• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.275-284
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• 2006

The noise sources of a Hyperion image are mainly due to the atmospheric effects, the sensor's instrumental errors, and A/D conversion. Though uncalibrated, overlapping, and all deep water absorption bands generally are removed, there still exist noisy bands. The visual inspection for selecting clean and stable processing bands is a simple practice, but is a manual, inefficient, and subjective process. In this paper, we propose that the extrema ratio be used for noise estimation and unsupervised band selection. The extrema ratio was compared with existing SNR and entropy measures. First, Gaussian, salt and pepper, and Speckle noises were added to ALI (Advanced Land Imager) images with relatively low noises, and the relation of noise level and those measures was explored. Second, the unsupervised band selection was performed through the EM (Expectation-Maximization) algorithm of the measures which were extracted from a Hyperion images. The Hyperion data were classified into 5 categories according to the image quality by visual inspection, and used as the reference data. The experimental result showed that the extrema ratio could be used effectively for band selection of Hyperion images.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1307-1309
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• 2003

The EO-1 spacecraft, launched November 21, 2000 into a sun synchronous orbit behind Landsat 7, hosts advanced technology demonstration instruments, whose capabilities are currently being assessed by the user community for future missions. A significant part of the EO-1 program is to perform data comparisons between Hyperion, ALI and Landsat 7 ETM+. In this paper, a comparison of forest classification results from Hyperion, ALI, and the ETM+ of Landsat-7 are provided for Wangqing Forest Bureau, Jilin Province, Northeast China. The data have been radiometrically corrected and geometrically resampled. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 86 channels to 14 features. Classes chosen for discrimination included Larch, Spruce, Oak, Birch, Popular and Mixed forest and other landuses. Classification accuracies have been obtained for each sensor. Comparison of the classification results shows : Hyperion classification results were the best, ALI's were much better than ETM+.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.289-297
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• 2006

The Land Cover/Land Use Map have been constructed from 1998, which has hierarchical structure according to land cover/land use system. Level 1 classification Map have done using Landsat satellite image over whole Korean peninsula. Level II classification Map have been digitized using IRS-1C, 1D, KOMPSAT and SPOT5 satellite images resolution-merged with low resolution color images. Level II Land Cover/Land Use Map construction by digitizing method, however, is consuming enormous expense for satellite image acquisition, image process and Land Cover/Land Use Map construction. In this paper, the possibility of constructing Level II Land Cover/Land Use Map using hyperspectral satellite image of EO-1 Hyperion, which is studied a lot recently, is studied. The comparison of classifications using Hyperion satellite image offering more spectral information and Landsat-7 ETM+ image is performed to evaluate the availability of Hyperion satellite image. Also, the algorithm of the optimal band selection is presented for effective application of hyperspectral satellite image.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.10-13
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• 2004

Hyperspectral data has a great advantage to classify various surface materials that are spectrally similar. In this study, we attempted to classify man-made materials in urban area using Hyperion data. Hyperion imagery of Seoul was initially processed to minimize radiometric distortions caused by sensor and atmosphere. Using color aerial photographs. we defined seven man-made surfaces (concrete, asphalt road. railroad, buildings, roof, soil, shadow) for the classification in Seoul. The hyperspectral data showed the potential to identify those manmade materials that were difficult to be classified by multispectral data. However. the classification of road and buildings was not quite satisfactory due to the relatively low spatial resolution of Hyperion image. Further, the low radiometric quality of Hyperion sensor was another limitation for the application in urban area.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1470-1472
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• 2003

Hyperion, as hyperspectral data, is carried on NASA’s EO-1 satellite, can be used in more subtle discrimination on forest cover, with 224 band in 360 ?2580 nm (10nm interval). In this study, Hyperion image is used to investigate the effects of topography on the classification of forest cover, and to assess whether the topographic correction improves the discrimination of species units for practical forest mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:25,000, is used to model the radiance variation on forest, considering MSR(Mean Spectral Ratio) on antithesis aspects. Hyperion, as hyperspectral data, is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. As a result, the approach on topographic effect normalization in hyperspectral data can effectively reduce the variation in detected radiance due to changes in forest illumination, progress the classification of forest cover.

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

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.7-14
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• 2007

Atmospheric correction is one of critical procedures to extract quantitative information related to biophysical variables from hyperspectral imagery. Most atmospheric correction algorithms developed for hyperspectral data have been based upon atmospheric radiative transfer (RT) codes, such as MODTRAN. Because of the difficulty in acquisition of atmospheric data at the time of image capture, the complexity of RT model, and large volume of hyperspectral data, atmospheric correction can be very difficult and time-consuming processing. In this study, we attempted to develop an efficient method for the atmospheric correction of EO-1 Hyperion data. This method uses the pre-calculated look-up-table (LUT) for fast and simple processing. The pre-calculated LUT was generated by successive running of MODTRAN model with several input parameters related to solar and sensor geometry, radiometric specification of sensor, and atmospheric condition. Atmospheric water vapour contents image was generated directly from a few absorption bands of Hyperion data themselves and used one of input parameters. This new atmospheric correction method was tested on the Hyperion data acquired on June 3, 2001 over Seoul area. Reflectance spectra of several known targets corresponded with the typical pattern of spectral reflectance on the atmospherically corrected Hyperion image, although further improvement to reduce sensor noise is necessary.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.63-70
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• 2015

An Image fusion, or Pansharpening is a methodology of increasing the spatial resolution of image with low-spatial resolution using high-spatial resolution images. In this paper, we have performed an image fusion of hyperspectral imagery by using panchromatic image with high-spatial resolution, multispectral and hyperspectral images with low-spatial resolution, which had been acquired by ALI and Hyperion of EO-1 satellite sensors. The study has been mainly focused on evaluating performance of fusion process following to the image fusion methodology of the block association, which had applied to ALI and Hyperion dataset by considering spectral characteristics between multispectral and hyperspectral images. The results from experiments have been identified that the proposed algorithm efficiently improved the spatial resolution and minimized spectral distortion comparing with results from a fusion of the only panchromatic and hyperspectral images and the existing block-based fusion method. Through the study in a proposed algorithm, we could concluded in that those applications of airborne hyperspectral sensors and various hyperspectral satellite sensors will be launched at future by enlarge its usages.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.489-495
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• 2010

Hyperspectral imaging obtains information with a wider wavelength range a large number of bands. However, a high correlation between each band, computation cost, and noise causes inaccurate results in cases of no pre-processing. The noises of band extraction and elimination positively necessary in hyperspectral imaging. Since the previous studies have used a characteristic the whole image, a local characteristic of the image is considered for the noise band extraction. In this study, the Quadtree, which is a data structure algorithm. and the fractal dimension are adopted for noise band extraction in Hyperion images. The fractal dimensions of the segments divided by the Quadtree structure are calculated, and variation is used. We focused on the extraction of random noise bands in Hyperion images and compared them with the reference data made by visual decisions. The proposed algorithm extracts the most bands, including random noises. It is possible to eliminate more than 30 noise bands, regardless of images.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.399-406
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• 2013

The quality of satellite imagery should be improved and stabilized to satisfy numerous users. The radiometric characteristics of an optical sensor can be a measure of data quality. In this study, a band aggregation technique and spectral response function of hyperspectral images are used to simulate multispectral images. EO-1 Hyperion and Landsat-8 OLI images acquired with about 30 minutes difference in overpass time were exploited to evaluate radiometric coefficients of OLI. Radiance values of the OLI and the simulated OLI were compared over three subsets covered by different land types. As a result, the index of agreement shows over 0.99 for all VNIR bands although there are errors caused by space/time and sensors.