• Title/Summary/Keyword: High-Resolution Hyperspectral Image

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High Resolution Reconstruction of EO-1 Hyperion Hyperspectral Images Using IKONOS Images (IKONOS 영상을 이용한 EO-1 Hyperion Hyperspectral 영상자료의 고해상도 구축)

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.24 no.6
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    • pp.631-639
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    • 2008
  • This study presents an approach to synthesize hyperspectral images of lower resolution at a higher resolution using the high resolution images acquired from a sensor of commercial satellites. The proposed method was applied to the reconstruction of EO-1 Hyperion images using the images acquired from IKONOS sensor. Based on the FitPAN-Mod pansharpening technique (Lee, 2008b), the hyperspectral images of 30m resolution were reconstructed at 1m resolution of IKONOS panchromatic image. In this study, the synthesized hyperspectral images of 50 bands, whose wavelengths range in the wavelength of panchromatic sensor, were generated from the three stages of high resolution reconstruction using FitPAN-Mod. The experimental results show that the proposed method effectively integrates the spatial detail of the panchromatic modality as well as the spectral detail of the hyperspectral one into the synthesized image. It indicates the proposed method has a potential as a technique to produce alternative images for the images that would have been observed from a hyperspectral sensor at the high resolution of commercial satellite images.

Evaluation of Block-based Sharpening Algorithms for Fusion of Hyperion and ALI Imagery (Hyperion과 ALI 영상의 융합을 위한 블록 기반의 융합기법 평가)

  • Kim, Yeji;Choi, Jaewan
    • 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.

A HIERARCHICAL APPROACH TO HIGH-RESOLUTION HYPERSPECTRAL IMAGE CLASSIFICATION OF LITTLE MIAMI RIVER WATERSHED FOR ENVIRONMENTAL MODELING

  • Heo, Joon;Troyer, Michael;Lee, Jung-Bin;Kim, Woo-Sun
    • Proceedings of the KSRS Conference
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    • v.2
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    • pp.647-650
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    • 2006
  • Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).

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A Study of Sub-Pixel Detection for Hyperspectral Image Using Linear Spectral Unmixing Algorithm (Linear Spectral Unmixing 기법을 이용한 하이퍼스펙트럴 영상의 Sub-Pixel Detection에 관한 연구)

  • 김대성;조영욱;한동엽;김용일
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2003.04a
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    • pp.161-166
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    • 2003
  • Hyperspectral imagery have high spectral resolution and provide the potential for more accurate and detailed information extraction than any other type of remotely sensed data. In this paper, the "Linear Spectral Unmixing" model which is one solution to overcome the limit of spatial resolution for remote sensing data was introduced and we applied the algorithm to hyperspectral image. The result was not good because of some problems such as image calibration and used endmembers. Therefore, we analyzed the cause and had a search for a solution.

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Mosaic image generation of AISA Eagle hyperspectral sensor using SIFT method (SIFT 기법을 이용한 AISA Eagle 초분광센서의 모자이크영상 생성)

  • Han, You Kyung;Kim, Yong Il;Han, Dong Yeob;Choi, Jae Wan
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.31 no.2
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    • pp.165-172
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    • 2013
  • In this paper, high-quality mosaic image is generated by high-resolution hyperspectral strip images using scale-invariant feature transform (SIFT) algorithm, which is one of the representative image matching methods. The experiments are applied to AISA Eagle images geo-referenced by using GPS/INS information acquired when it was taken on flight. The matching points between three strips of hyperspectral images are extracted using SIFT method, and the transformation models between images are constructed from the points. Mosaic image is, then, generated using the transformation models constructed from corresponding images. Optimal band appropriate for the matching point extraction is determined by selecting representative bands of hyperspectral data and analyzing the matched results based on each band. Mosaic image generated by proposed method is visually compared with the mosaic image generated from initial geo-referenced AISA hyperspectral images. From the comparison, we could estimate geometrical accuracy of generated mosaic image and analyze the efficiency of our methodology.

The Endmember Analysis for Sub-Pixel Detection Using the Hyperspectral Image

  • Kim, Dae-Sung;Cho, Young-Wook;Han, Dong-Yeob;Kim, Young-Il
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.732-734
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    • 2003
  • In the middle -resolution remote sensing, the Ground Sampled Distance(GSD) sensed and sampled by the detector is generally larger than the size of objects(or materials) of interest, in which case several objects are embedded in a single pixel and cannot be detected spatially. This study is intended to solve this problem of a hyperspectral data with high spectral resolution. We examined the detection algorithm, Linear Spectral Mixing Model, and also made a test on the Hyperion data. To find class Endmembers, we applied two methods, Spectral Library and Geometric Model, and compared them with each other.

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Hyperspectral Fluorescence Imaging for Mouse Skin Tumor Detection

  • Kong, Seong G.;Martin, Matthew E.;Vo-Dinh, Tuan
    • ETRI Journal
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    • v.28 no.6
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    • pp.770-776
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    • 2006
  • This paper presents a hyperspectral imaging technique based on laser-induced fluorescence for non-invasive detection of tumorous tissue on mouse skin. Hyperspectral imaging sensors collect image data in a number of narrow, adjacent spectral bands. Such high-resolution measurement of spectral information reveals contiguous emission spectra at each image pixel useful for the characterization of constituent materials. The hyperspectral image data used in this study are fluorescence images of mouse skin consisting of 21 spectral bands in the visible spectrum of the wavelengths ranging from 440 nm to 640 nm. Fluorescence signal is measured with the use of laser excitation at 337 nm. An acousto-optic tunable filter (AOTF) is used to capture images at 10 nm intervals. All spectral band images are spatially registered with the reference band image at 490 nm to obtain exact pixel correspondences by compensating the spatial offsets caused by the refraction differences in AOTF at different wavelengths during the image capture procedure. The unique fluorescence spectral signatures demonstrate a good separation to differentiate malignant tumors from normal tissues for rapid detection of skin cancers without biopsy.

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Hyperspectral Image Fusion Algorithm Based on Two-Stage Spectral Unmixing Method (2단계 분광혼합기법 기반의 하이퍼스펙트럴 영상융합 알고리즘)

  • Choi, Jae-Wan;Kim, Dae-Sung;Lee, Byoung-Kil;Yu, Ki-Yun;Kim, Yong-Il
    • Korean Journal of Remote Sensing
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    • v.22 no.4
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    • pp.295-304
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    • 2006
  • Image fusion is defined as making new image by merging two or more images using special algorithms. In case of remote sensing, it means fusing multispectral low-resolution remotely sensed image with panchromatic high-resolution image. Generally, hyperspectral image fusion is accomplished by utilizing fusion technique of multispectral imagery or spectral unmixing model. But, the former may distort spectral information and the latter needs endmember data or additional data, and has a problem with not preserving spatial information well. This study proposes a new algorithm based on two stage spectral unmixing model for preserving hyperspectral image's spectral information. The proposed fusion technique is implemented and tested using Hyperion and ALI images. it is shown to work well on maintaining more spatial/spectral information than the PCA/GS fusion algorithms.

EO-1 Hyperion / Landsat-7 ETM+ 영상을 활용한 영상분류 정확도 분석

  • Jang Se-Jin;Chae Ok-Sam
    • Proceedings of the KSRS Conference
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    • 2006.03a
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    • pp.223-227
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    • 2006
  • 최근 위성기술의 발전은 크게 두 가지 방향으로 진행되고 있다. 하나는 고해상도(High Resolution)라는 말로 대표되는 공간해상도(Spatial Resolution)의 향상이고, 다른 하나는 초분광(Hyperspectral)으로 대표되는 분광해상도(Spectral Resolution)의 향상이다. 특히 초분광영상(Hyperspectral Image)은 지상피복 및 대상물에 대해 실험실에서 얻을 수 있을 정도의 연속적이고 좁은 파장 간격의 분광정보를 제공하고 있어, 기존에 사용하던 다중분광영상(Multispectral Image) 보다 많은 양의 정보를 사용자에게 제공한다. 본 논문에서는 다중분광영상과 초분광영상의 분광 정보를 활용한 영상분류능력을 비교분석하고 그 결과를 평가하였다. 분석결과는 다중분광영상에서 식별이 어려웠던 초지, 농지, 나지에 대한 분석 능력이 초분광영상에서 상당히 향상됨으로써 감독분류에서 약 20% 정도의 정확도 향상을 가져왔으며, 무감독분류의 경우에는 미소한 차이로 그 정확도가 향상된다는 것이다. 이런 결과는 향후 초분광영상의 토지 피복분류 및 대상물 탐사에 긍정적인 활용 방안을 제시할 수 있음을 알려주고 있다.

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A Study on Linear Spectral Mixing Model for Hyperspectral Imagery with Geometric Method (기하학적 기법을 이용한 하이퍼스펙트럴 영상의 Linear Spectral Mixing모델에 관한 연구)

  • 장은석;김대성;김용일
    • Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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    • 2003.11a
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    • pp.23-29
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    • 2003
  • Detection in remotely sensed images can be conducted spatially, spectrally or both [2]. If the images have high spatial resolution, materials can be detected by using spatial and spectral information, unless we can't see the object embedded in a pixel. In this paper, we intend to solve the limit of spatial resolution by using the hyperspectral image which has high spectral resolution. Therefore, the Linear Spectral Mixing(LSM) Model which is sub-pixel detection algorithm is used to solve this problem. To find class Endmembers, we applied Geometric Model with MNF(Minimum Noise Fraction) transformation. From the result of sub-pixel detection algorithm, we can see the detection of water is satisfied and the object shape cannot be extracted but the possibility of material existence can be identified.

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