• Title/Summary/Keyword: RapidEye Satellite Image

Search Result 24, Processing Time 0.019 seconds

Impervious Surface Mapping of Cheongju by Using RapidEye Satellite Imagery (RapidEye 위성영상을 이용한 청주시의 불투수면지도 생성기법)

  • Park, Hong Lyun;Choi, Jae Wan;Choi, Seok Keun
    • Journal of Korean Society for Geospatial Information Science
    • /
    • v.22 no.1
    • /
    • pp.71-79
    • /
    • 2014
  • Most researches have created the impervious surface map by using low-spatial-resolution satellite imagery and are inefficient to generate the object-based impervious map with a broad area. In this study, segment-based impervious surface mapping algorithm is proposed using the RapidEye satellite imagery in order to map impervious area. At first, additional bands are generated by using TOA reflectance conversion RapidEye data. And then, shadow and water class are extracted using training data of converted reflectance image. Object-based impervious surface can be generated by spectral mixture analysis based on land cover map of Ministry of Environment with medium scale, in the case of other classes except shadow and water classes. The experiment shows that result by our method represents high classification accuracy compared to reference data, quantitatively.

Automated Image Matching for Satellite Images with Different GSDs through Improved Feature Matching and Robust Estimation (특징점 매칭 개선 및 강인추정을 통한 이종해상도 위성영상 자동영상정합)

  • Ban, Seunghwan;Kim, Taejung
    • Korean Journal of Remote Sensing
    • /
    • v.38 no.6_1
    • /
    • pp.1257-1271
    • /
    • 2022
  • Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

Extraction of Spatial Characteristics of Cadastral Land Category from RapidEye Satellite Images

  • La, Phu Hien;Huh, Yong;Eo, Yang Dam;Lee, Soo Bong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.32 no.6
    • /
    • pp.581-590
    • /
    • 2014
  • With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

An Implementation of the OTB Extension to Produce RapidEye Surface Reflectance and Its Accuracy Validation Experiment (RapidEye 영상정보의 지표반사도 생성을 위한 OTB Extension 개발과 정확도 검증 실험)

  • Kim, Kwangseob;Lee, Kiwon
    • Korean Journal of Remote Sensing
    • /
    • v.38 no.5_1
    • /
    • pp.485-496
    • /
    • 2022
  • This study is for the software implementation to generate atmospheric and surface reflectance products from RapidEye satellite imagery. The software is an extension based on Orfeo Toolbox (OTB) and an open-source remote sensing software including calibration modules which use an absolute atmospheric correction algorithm. In order to verify the performance of the program, the accuracy of the product was validated by a test image on the Radiometric Calibration Network (RadCalNet) site. In addition, the accuracy of the surface reflectance product generated from the KOMPSAT-3A image, the surface reflectance of Landsat Analysis Ready Data (ARD) of the same site, and near acquisition date were compared with RapidEye-based one. At the same time, a comparative study was carried out with the processing results using QUick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) tool supported by a commercial tool for the same image. Similar to the KOMPSAT-3A-based surface reflectance product, the results obtained from RapidEye Extension showed accuracy of agreement level within 5%, compared with RadCalNet data. They also showed better accuracy in all band images than the results using QUAC or FLAASH tool. As the importance of the Red-Edge band in agriculture, forests, and the environment applications is being emphasized, it is expected that the utilization of the surface reflectance products of RapidEye images produced using this program will also increase.

Automated Improvement of RapidEye 1-B Geo-referencing Accuracy Using 1:25,000 Digital Maps (1:25,000 수치지도를 이용한 RapidEye 위성영상의 좌표등록 정확도 자동 향상)

  • Oh, Jae Hong;Lee, Chang No
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.32 no.5
    • /
    • pp.505-513
    • /
    • 2014
  • The RapidEye can acquire the 6.5m spatial resolution satellite imagery with the high temporal resolution on each day, based on its constellation of five satellites. The image products are available in two processing levels of Basic 1B and Ortho 3A. The Basic 1B image have radiometric and sensor corrections and include RPCs (Rational Polynomial Coefficients) data. In Korea, the geometric accuracy of RapidEye imagery can be improved, based on the scaled national digital maps that had been built. In this paper, we present the fully automated procedures to georegister the 1B data using 1:25,000 digital maps. Those layers of map are selected if the layers appear well in the RapidEye image, and then the selected layers are RPCs-projected into the RapidEye 1B space for generating vector images. The automated edge-based matching between the vector image and RapidEye improves the accuracy of RPCs. The experimental results showed the accuracy improvement from 2.8 to 0.8 pixels in RMSE when compared to the maps.

RNCC-based Fine Co-registration of Multi-temporal RapidEye Satellite Imagery (RNCC 기반 다시기 RapidEye 위성영상의 정밀 상호좌표등록)

  • Han, Youkyung;Oh, Jae Hong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.36 no.6
    • /
    • pp.581-588
    • /
    • 2018
  • The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

Determination of Spatial Resolution to Improve GCP Chip Matching Performance for CAS-4 (농림위성용 GCP 칩 매칭 성능 향상을 위한 위성영상 공간해상도 결정)

  • Lee, YooJin;Kim, Taejung
    • Korean Journal of Remote Sensing
    • /
    • v.37 no.6_1
    • /
    • pp.1517-1526
    • /
    • 2021
  • With the recent global and domestic development of Earth observation satellites, the applications of satellite images have been widened. Research for improving the geometric accuracy of satellite images is being actively carried out. This paper studies the possibility of automated ground control point (GCP) generation for CAS-4 satellite, to be launched in 2025 with the capability of image acquisition at 5 m ground sampling distance (GSD). In particular, this paper focuses to check whether GCP chips with 25 cm GSD established for CAS-1 satellite images can be used for CAS-4 and to check whether optimalspatial resolution for matching between CAS-4 images and GCP chips can be determined to improve matching performance. Experiments were carried out using RapidEye images, which have similar GSD to CAS-4. Original satellite images were upsampled to make satellite images with smaller GSDs. At each GSD level, up-sampled satellite images were matched against GCP chips and precision sensor models were estimated. Results shows that the accuracy of sensor models were improved with images atsmaller GSD compared to the sensor model accuracy established with original images. At 1.25~1.67 m GSD, the accuracy of about 2.4 m was achieved. This finding lead that the possibility of automated GCP extraction and precision ortho-image generation for CAS-4 with improved accuracy.

A Study on the Extraction of a River from the RapidEye Image Using ISODATA Algorithm (ISODATA 기법을 이용한 RapidEye 영상으로부터 하천의 추출에 관한 연구)

  • Jo, Myung-Hee
    • Journal of the Korean Association of Geographic Information Studies
    • /
    • v.15 no.4
    • /
    • pp.1-14
    • /
    • 2012
  • A river is defined as the watercourse flowing through its channel, and the mapping tasks of a river plays an important role for the research on the topographic changes in the riparian zones and the research on the monitoring of flooding in its floodplain. However, the utilization of the ground surveying technologies is not efficient for the mapping tasks of a river due to the irregular surfaces of the riparian zones and the dynamic changes of water level of a river. Recently, the spatial information data sets are widely used for the coastal mapping tasks due to the acquisition of the topographic information without human accessibility. In this research, we tried to extract a river from the RapidEye imagery by using the ISODATA(Iterative Self_Organizing Data Analysis) classification algorithm with the two different parameters(NIR (Near Infra-Red) band and NDVI(Normalized Difference Vegetation Index)). First, the two different images(the NIR band image and the NDVI image) were generated from the RapidEye imagery. Second, the ISODATA algorithm were applied to each image and each river was generated in each image through the post-processing steps. River boundaries were also extracted from each classified image using the Sobel edge detection algorithm. Ground truths determined by the experienced expert are used for the assessment of the accuracy of an each generated river. Statistical results show that the extracted river using the NIR band has higher accuracies than the extracted river using the NDVI.

Vicarious Radiometric Calibration of RapidEye Satellite Image Using CASI Hyperspectral Data (CASI 초분광 영상을 이용한 RapidEye 위성영상의 대리복사보정)

  • Chang, An Jin;Choi, Jae Wan;Song, Ah Ram;Kim, Ye Ji;Jung, Jin Ha
    • Journal of Korean Society for Geospatial Information Science
    • /
    • v.23 no.3
    • /
    • pp.3-10
    • /
    • 2015
  • All kinds of objects on the ground have inherent spectral reflectance curves, which can be used to classify the ground objects and to detect the target. Remotely sensed data have to be transferred to spectral reflectance for accurate analysis. There are formula methods provided by the institution, mathematical model method and ground-data-based method. In this study, RapidEye satellite image was converted to reflectance data using spectral reflectance of a CASI hyperspectral image by using vicarious radiometric calibration. The results were compared with those of the other calibration methods and ground data. The proposed method was closer to the ground data than ATCOR and New Kurucz 2005 method and equal with ELM method.

A Study on Object-Based Image Analysis Methods for Land Cover Classification in Agricultural Areas (농촌지역 토지피복분류를 위한 객체기반 영상분석기법 연구)

  • Kim, Hyun-Ok;Yeom, Jong-Min
    • Journal of the Korean Association of Geographic Information Studies
    • /
    • v.15 no.4
    • /
    • pp.26-41
    • /
    • 2012
  • It is necessary to manage, forecast and prepare agricultural production based on accurate and up-to-date information in order to cope with the climate change and its impacts such as global warming, floods and droughts. This study examined the applicability as well as challenges of the object-based image analysis method for developing a land cover image classification algorithm, which can support the fast thematic mapping of wide agricultural areas on a regional scale. In order to test the applicability of RapidEye's multi-temporal spectral information for differentiating agricultural land cover types, the integration of other GIS data was minimized. Under this circumstance, the land cover classification accuracy at the study area of Kimje ($1300km^2$) was 80.3%. The geometric resolution of RapidEye, 6.5m showed the possibility to derive the spatial features of agricultural land use generally cultivated on a small scale in Korea. The object-based image analysis method can realize the expert knowledge in various ways during the classification process, so that the application of spectral image information can be optimized. An additional advantage is that the already developed classification algorithm can be stored, edited with variables in detail with regard to analytical purpose, and may be applied to other images as well as other regions. However, the segmentation process, which is fundamental for the object-based image classification, often cannot be explained quantitatively. Therefore, it is necessary to draw the best results based on expert's empirical and scientific knowledge.