• Title, Summary, Keyword: Multispectral sensors

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Image Fusion Methods for Multispectral and Panchromatic Images of Pleiades and KOMPSAT 3 Satellites

  • Kim, Yeji;Choi, Jaewan;Kim, Yongil
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.5
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    • pp.413-422
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    • 2018
  • Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.

Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis

  • Yeji, Kim;Jaewan, Choi;Anjin, Chang;Yongil, Kim
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.33 no.3
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    • pp.211-218
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    • 2015
  • The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

Performance Evaluation of Pansharpening Algorithms for WorldView-3 Satellite Imagery

  • Kim, Gu Hyeok;Park, Nyung Hee;Choi, Seok Keun;Choi, Jae Wan
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.34 no.4
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    • pp.413-423
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    • 2016
  • Worldview-3 satellite sensor provides panchromatic image with high-spatial resolution and 8-band multispectral images. Therefore, an image-sharpening technique, which sharpens the spatial resolution of multispectral images by using high-spatial resolution panchromatic images, is essential for various applications of Worldview-3 images based on image interpretation and processing. The existing pansharpening algorithms tend to tradeoff between spectral distortion and spatial enhancement. In this study, we applied six pansharpening algorithms to Worldview-3 satellite imagery and assessed the quality of pansharpened images qualitatively and quantitatively. We also analyzed the effects of time lag for each multispectral band during the pansharpening process. Quantitative assessment of pansharpened images was performed by comparing ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), SAM (Spectral Angle Mapper), Q-index and sCC (spatial Correlation Coefficient) based on real data set. In experiment, quantitative results obtained by MRA (Multi-Resolution Analysis)-based algorithm were better than those by the CS (Component Substitution)-based algorithm. Nevertheless, qualitative quality of spectral information was similar to each other. In addition, images obtained by the CS-based algorithm and by division of two multispectral sensors were shaper in terms of spatial quality than those obtained by the other pansharpening algorithm. Therefore, there is a need to determine a pansharpening method for Worldview-3 images for application to remote sensing data, such as spectral and spatial information-based applications.

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.

Detection Method of River Floating Debris Using Unmanned Aerial Vehicle and Multispectral Sensors (무인항공기 및 다중분광센서를 이용한 하천부유쓰레기 탐지 기법 연구)

  • Kim, Heung-Min;Yoon, HongJoo;Jang, SeonWoong;Chung, YongHyun
    • Korean Journal of Remote Sensing
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    • v.33 no.5_1
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    • pp.537-546
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    • 2017
  • This study aims to develop the floating debris detection algorithm using a Unmanned Aerial Vehicle (UAV) and multispectral sensors. In addition, the occurrence range of floating debris was estimated by applying the algorithm. An aerial photograph using an unmanned aerial vehicle was used to generate an orthoimage that can calculate the area. A spectrum survey of water, plants litter, polystyrene foam etc. was conducted. After obtaining spectroscopic characteristics of floating debris and water, the River Floating Debris (RFD) index was calculated. And we detected the floating debris through band combination of sensor using RFD. As a result of the RFD application, accumulation zone of floating debris was confirmed at three sites in the orthoimage. It was estimated that a lot of floating debris was accumulated at 0.82 ha ($8,200m^2$), which is corresponding to 3.6% including the accumulation zone.

Absolute Radiometric Calibration for KOMPSAT-3 AEISS and Cross Calibration Using Landsat-8 OLI

  • Ahn, Hoyong;Shin, Dongyoon;Lee, Sungu;Choi, Chuluong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.35 no.4
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    • pp.291-302
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    • 2017
  • Radiometric calibration is a prerequisite to quantitative remote sensing, and its accuracy has a direct impact on the reliability and accuracy of the quantitative application of remotely sensed data. This paper presents absolute radiometric calibration of the KOMPSAT-3 (KOrea Multi Purpose SATellite-3) and cross calibration using the Landsat-8 OLI (Operational Land Imager). Absolute radiometric calibration was performed using a reflectance-based method. Correlations between TOA (Top Of Atmosphere) radiances and the spectral band responses of the KOMPSAT-3 sensors in Goheung, South Korea, were significant for multispectral bands. A cross calibration method based on the Landsat-8 OLI was also used to assess the two sensors using near simultaneous image pairs over the Libya-4 PICS (Pseudo Invariant Calibration Sites). The spectral profile of the target was obtained from EO-1 (Earth Observing-1) Hyperion data over the Libya-4 PICS to derive the SBAF (Spectral Band Adjustment Factor). The results revealed that the TOA radiance of the KOMPSAT-3 agree with Landsat-8 within 5.14% for all bands after applying the SBAF. The radiometric coefficient presented here appears to be a good standard for maintaining the optical quality of the KOMPSAT-3.

The comparison of spatial/spectral distortion on the hybrid pansharpened images by the spatial correlation methods (공간 상관도 기법에 따른 하이브리드 융합영상의 공간/분광 왜곡 평가)

  • Choi, Jae-Wan;Kim, Dae-Sung;Kim, Yong-Ii
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.29 no.2
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    • pp.175-181
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    • 2011
  • In remote sensing, it has been a difficult task to obtain a multispectral image with high spatial resolution because of the technical limitation of satellite sensors. In order to solve these problems, various pansharpening algorithms have been tried and proposed. However, most pansharpened images created by various approaches tend to distort the spectral characteristics of the original multispectral image or decrease the visual sharpness of the panchromatic image. To minimize the spectral distortion of pansharpened image while preserving spatial information of the panchromatic image, a hybrid pansharpening algorithm based on the spatial correlation was proposed. In this paper, we analyzed the spatial and spectral distortion of the hybrid pansharpened images generated by the various spatial correlation methods. In the experiments, we proved that the method by using Laplacian filtering was more efficient than other high frequency extraction algorithms in the viewpoint of spectral distortion and spatial sharpness.

Reflectance Measurements of Soil Variability

  • Sudduth, K.A.;Hong, S.Y.;Hummel, J.W.;Kitchen, N.R.
    • Proceedings of the KSRS Conference
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    • pp.1194-1196
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    • 2003
  • Variations in soil physical and chemical properties can affect agricultural productivity and the environmental implications of crop production. These variations are present and may be important at regional, field, and sub-field (precision agriculture) scales. Because traditional measurements are time-consuming and expensive, reflectance-based estimates of soil properties such as texture, organic matter content, water content, and nutrient status are attractive. Soil properties have been related to reflectance measured with laboratory, in-field, airborne, and satellite sensors. Both multispectral and hyperspectral instruments have been used, with both natural and artificial illumination. Varying levels of accuracy have been obtained, with the best results (r > 0.95) using hyperspectral reflectance data to estimate soil organic matter and water content.

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Spectral Sensing for Plant Stress Assessment - A Review -

  • Kim, Y.;Reid, J.F.
    • Agricultural and Biosystems Engineering
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    • v.7 no.1
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    • pp.27-41
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    • 2006
  • Assessment of nitrogen and chlorophyll content from crop leaves can help growers adjust N fertilizer rates to meet the demands of the crop. Numerous researchers have presented their studies about spectral signature of plant leaves to characterize the plant features. However, interrelational review and summary were limited and a communication gap exists between the plant science and optical engineering. Understanding the mechanism of leaf interaction to electromagnetic radiation and factors affecting spectrophotometric measurements can enhance the foundation of optical remote sensing technologies. This paper provides extensive review of previous works in optical sensing and explains the basics of plant optics, spectral measurements for plant stress, factors that affect sensitivity to spectral analysis, and applications that deploy optical remote sensing technologies.

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Hierarchical Clustering Approach of Multisensor Data Fusion: Application of SAR and SPOT-7 Data on Korean Peninsula

  • Lee, Sang-Hoon;Hong, Hyun-Gi
    • Proceedings of the KSRS Conference
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    • pp.65-65
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    • 2002
  • In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

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