Araştırma Makalesi
BibTex RIS Kaynak Göster

Using of high-resolution satellite images in object-based image analysis

Yıl 2019, Cilt: 7 Sayı: 2, 187 - 204, 20.08.2019
https://doi.org/10.31195/ejejfs.603510

Öz

Remote Sensing technologies have been used quite a
long time in forestry applications. While the more acquired data can be
obtained with traditional survey and photogrammetric techniques, they required
relatively more manpower and time consuming.



The most important characteristics of this research
will bring the new opportunities for forestry applications by using the
object-based classification methods with multispectral satellite images that
have high spatial resolution (<1meter). In this individual tree and forest
stand based research, the solutions searched with using very high-resolution
(VHR) satellite images for time-consuming problems in forestry applications.


Destekleyen Kurum

Scientific Research Projects Coordination Unit of Istanbul University

Proje Numarası

9895

Teşekkür

This work was supported by Scientific Research Projects Coordination Unit of Istanbul University. The project number is 9895. This paper is based in part on a PhD thesis by one of the authors (H. Yurtseven).

Kaynakça

  • Aplin P., Atkinson P. M., Curran P. J. (1999). Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the United Kingdom. Remote Sensing of Environment 68:(3) 206-216
  • Baatz M., Benz U., Dehghani S., Heynen M., Höltje A., Hofmann P., Lingenfelder I., Mimler M., Sohlbach M., Weber M. (2004). eCognition professional user guide 4. Definiens Imaging. Definiens Imaging Munich.
  • Baatz M., Schäpe A. (1999). Object-oriented and multi-scale image analysis in semantic networks. 2nd international symposium: operationalization of remote sensing.
  • Bakker W. H., Janssen L. L. F., Reeves C. V., Gorte B. G. H., Christine P., Weir M. J. C., Horn J. A., Anupma P., Tsehaie W. (2009). Principles of Remote Sensing: An Introductory Textbook. 4th Ed. ed. ITC Educational Textbook Series. Enschede, The Netherlands.
  • Baltsavias E. P. (2004). Object extraction and revision by image analysis using existing geodata and knowledge: Current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing 58:(3-4) 129-151
  • Blaschke T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65:(1) 2-16. http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004.
  • Blaschke T., Hay G. J., Kelly M., Lang S., Hofmann P., Addink E., Queiroz Feitosa R., van der Meer F., van der Werff H., van Coillie F., Tiede D. (2014). Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing 87:(0) 180-191. http://dx.doi.org/10.1016/j.isprsjprs.2013.09.014.
  • Blaschke T., Lang S., Hay G. J. (2008). Object-based image analysis : spatial concepts for knowledge-driven remote sensing applications. 1st edSpringer New York.
  • Brandtberg T., Walter F. (1998). Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications 11:(2) 64-73
  • Câmara G., Souza R. C. M., Freitas U. M., Garrido J. (1996). Spring: Integrating remote sensing and gis by object-oriented data modelling. Computers and Graphics (Pergamon) 20:(3) 395-403
  • Campbell J. B., Wynne R. H. (2012). Introduction to Remote Sensing. 5th edGuilford Publications.
  • Chubey M. S., Franklin S. E., Wulder M. A. (2006). Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing 72:(4) 383
  • Cliche G., Bonn F., Teillet P. (1985). Integration of the SPOT panchromatic channel into its multispectral mode for image sharpness enhancement. Photogrammetric Engineering and Remote Sensing 51: 311-316
  • Culvenor D. S. (2002). TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Computers & Geosciences 28:(1) 33-44
  • de Béthune S., Muller F., Donnay J.-P. (1998). Fusion of multispectral and panchromatic images by local mean and variance matching filtering techniques. Fusion of Earth Data 28-30
  • Descombes X., Pechersky E. (2006). Tree crown extraction using a three states Markov random field. INRIA.
  • Deskevich M. P., Padwick C. (2012). System and Method For Combining Color Information With Spatial Information In Multispectral Images. United States.
  • Erdin K. (1986). Fotoyorumlama ve Uzaktan Algılamaİstanbul Üniversitesi Orman Fakültesi Yayınları İstanbul.
  • Erikson M. (2003). Segmentation of individual tree crowns in colour aerial photographs using region growing supported by fuzzy rules. Canadian Journal of Forest Research 33:(8) 1557-1563
  • Garguet-Duport B., Girel J., Chassery J.-M., Patou G. (1996). The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing 62:(9) 1057-1066
  • Gougeon F. A. (1995). A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing 21:(3) 274–284
  • Gudex-Cross D., Pontius J., Adams A. (2017). Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery. Remote Sensing of Environment 196: 193-204. https://doi.org/10.1016/j.rse.2017.05.006.
  • Haralick R. M. (1983). Decision Making In Context. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5:(4) 417-428
  • Haralick R. M., Shapiro L. G. (1985). Image Segmentation Techniques. Computer Vision, Graphics, and Image Processing 29:(1) 100-132
  • Hay G., Niemann K., McLean G. (1996). An object-specific image-texture analysis of H-resolution forest imagery. Remote Sensing of Environment 55:(2) 108-122
  • Hayes D. J., Sader S. A., Schwartz N. B. (2002). Analyzing a forest conversion history database to explore the spatial and temporal characteristics of land cover change in Guatemala's Maya Biosphere Reserve. Landscape Ecology 17:(4) 299-314
  • Henrich V., Krauss G., Götze C., Sandow C. (2015). IDB - Index Database: A database for remote sensing indices.
  • Huete A., Liu H., Batchily K., Van Leeuwen W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59:(3) 440-451
  • Immitzer M., Vuolo F., Atzberger C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing 8:(3). 10.3390/rs8030166.
  • Jackson R., Slater P., Pinter Jr P. (1983). Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of Environment 13:(3) 187-208
  • Jones D., Pike S., Thomas M., Murphy D. (2011). Object-based image analysis for detection of Japanese knotweed sl taxa (Polygonaceae) in Wales (UK). Remote Sensing 3:(2) 319-342
  • Kaczynski R., Donnay J.-P., Muller F. (1995). Satellite image maps of Warsaw in the scale 1: 25,000. EARSeL Advances in Remote Sensing 4: 100-102
  • Kaufman Y. J., Tanré D. (1996). Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sensing of Environment 55:(1) 65-79
  • Ke Y., Quackenbush L. J. (2007). Forest species classification and tree crown delineation using Quickbird imagery. Proceedings of the ASPRS 2007 Annual Conference.
  • Kettig R. L., Landgrebe D. (1976). Classification of multispectral image data by extraction and classification of homogeneous objects. Geoscience Electronics, IEEE Transactions on 14:(1) 19-26
  • Koç A. (1997). Belgrad Ormanındaki Ağaç Türü Ve Karışımlarının Uydu Verileri Ve Görüntü İşleme Teknikleri İle Belirlenmesi. İ.Ü. Orman Fakültesi Dergisi A/47:(1) 89-110
  • Levine M. D., Nazif A. M. (1985). Rule-based image segmentation: a dynamic control strategy approach. Computer Vision, Graphics, & Image Processing 32:(1) 104-126
  • Li D., Ke Y., Gong H., Li X. (2015). Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images. Remote Sensing 7:(12). 10.3390/rs71215861.
  • Lillesand T., Kiefer R. W., Chipman J. (2014). Remote Sensing and Image Interpretation. 7th edWiley.Lobo A. (1997). Image segmentation and discriminant analysis for the identification of land cover units in ecology. Geoscience and Remote Sensing, IEEE Transactions on 35:(5) 1136-1145
  • Lobo A., Chic O., Casterad A. (1996). Classification of Mediterranean crops with multisensor data: Per-pixel versus per-object statistics and image segmentation. International Journal of Remote Sensing 17:(12) 2385-2400
  • McKeown Jr D. M., Harvey W. A., Wixson L. E. (1989). Automating knowledge acquisition for aerial image interpretation. Computer Vision, Graphics, & Image Processing 46:(1) 37-81
  • Navulur K. (2007). Multispectral Image Analysis Using the Object-Oriented ParadigmCRC Press/Taylor & Francis.
  • Neubert M., Meinel G. (2005). Atmospheric and terrain correction of Ikonos imagery using ATCOR3. Leibniz Institute of Ecological and Regional Development. Dresden, Germany
  • Nussbaum S. (2008). Object-based image analysis and treaty verification : new approaches in remote sensing - applied to nuclear facilities in iran. Is edSpringer Berlin Heidelberg New York, NY.
  • Padwick C., Deskevich M., Pacifici F., Smallwood S. (2010). WorldView-2 pan-sharpening. Proc. American Society for Photogrammetry and Remote Sensing 13
  • Pal N. R., Pal S. K. (1993). A review on image segmentation techniques. Pattern Recognition 26:(9) 1277-1294
  • Perrin G., Descombes X., Zerubia J. (2006). A non-Bayesian model for tree crown extraction using marked point processes. INRIA.
  • Pinz A. (1989). Final results of the vision expert system VES: finding trees in aerial photographs. Wissensbasierte Mustererkennung. Wien 49: 90-111
  • Pollock R. J. (1996). The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model. The University of British Columbia (Canada).
  • Rafieyan O., Darvishsefat A., Babaii S. (2009a). Evaluation of object-based classification method in forest applications using UltraCamD imagery (Case study: Northern forest of Iran). 3rd National Forest Congress, University of Tehran, Karaj, Iran.
  • Rafieyan O., Darvishsefat A., Babaii S., Mataji A. (2009b). Object-based classification using UltraCam-D images for tree species discrimination (Case study: Hyrcanian Forest-Iran). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38:
  • Richter R., Schläpfer D. (2014). Atmospheric/Topographic Correction For Satellite Imagery. DLR report DLR-IB. ReSe Applications Schlapfer Switzerland.
  • Rouse Jr J., Haas R., Schell J., Deering D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 351: 309
  • Ryherd S., Woodcock C. (1996). Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric Engineering and Remote Sensing 62:(2) 181-194
  • Sader S., Winne J. (1992). RGB-NDVI colour composites for visualizing forest change dynamics. International Journal of Remote Sensing 13:(16) 3055-3067
  • Sandmann H., Lertzman K. P. (2003). Combining high-resolution aerial photography with gradient-directed transects to guide field sampling and forest mapping in mountainous terrain. Forest Science 49:(3) 429-443
  • Schott J. R. (2007). Remote sensing : the image chain approach. 2nd edOxford University Press New York.
  • Sellers P. (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6:(8) 1335-1372
  • Strahler A. H., Woodcock C. E., Smith J. A. (1986). On the nature of models in remote sensing. Remote Sensing of Environment 20:(2) 121-139
  • Trimble (2012). eCognition Developer User GuideTrimble Germany GmbH München.
  • Tucker C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8:(2) 127-150
  • Updike T., Comp C. (2010). Radiometric use of WorldView-2 imagery. Technical Note 1-17
  • Welch R., Ehlers M. (1987). Merging multiresolution SPOT HRV and Landsat TM data. Photogrammetric Engineering and Remote Sensing 53: 301-303
  • Wolf A. (2010). Using WorldView 2 Vis-NIR MSI imagery to support land mapping and feature extraction using normalized difference index ratios. DigitalGlobe 8-Band Research Challenge 1-13
  • Wong T., Mansor S., Mispan M., Ahmad N., Sulaiman W. (2003). Feature extraction based on object oriented analysis. Proceedings of ATC 2003 Conference.
  • Wu B., Yu B., Wu Q., Huang Y., Chen Z., Wu J. (2016). Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. International Journal of Applied Earth Observation and Geoinformation 52: 82-94
  • Wulder M. (1998). Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography 22:(4) 449-476
  • Wulder M. A., White J. C., Hay G. J., Castilla G. (2008). Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery. The Forestry Chronicle 84:(2) 221-230. 10.5558/tfc84221-2.
  • Yan G., Mas J. F., Maathuis B., Xiangmin Z., Van Dijk P. (2006). Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing 27:(18) 4039-4055
  • Yu Q., Gong P., Clinton N., Biging G., Kelly M., Schirokauer D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing 72:(7) 799-811
  • Yurtseven H. (2014). Yüksek Çözünürlüklü Uydu Verileri ile Obje Tabanlı Görüntü Analizleri. Fen Bilimleri Enstitüsü. İstanbul, Türkiye, İstanbul Üniversitesi. 147.
  • Zhou X., Jancsó T., Chen C., Verőné M. W. (2012). Urban Land Cover Mapping Based on Object Oriented Classification Using WorldView 2 Satellite Remote Sensing Images. International Scientific Conference on Sustainable Development & Ecological Footprint, Sopron, Hungary.
Yıl 2019, Cilt: 7 Sayı: 2, 187 - 204, 20.08.2019
https://doi.org/10.31195/ejejfs.603510

Öz

Proje Numarası

9895

Kaynakça

  • Aplin P., Atkinson P. M., Curran P. J. (1999). Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the United Kingdom. Remote Sensing of Environment 68:(3) 206-216
  • Baatz M., Benz U., Dehghani S., Heynen M., Höltje A., Hofmann P., Lingenfelder I., Mimler M., Sohlbach M., Weber M. (2004). eCognition professional user guide 4. Definiens Imaging. Definiens Imaging Munich.
  • Baatz M., Schäpe A. (1999). Object-oriented and multi-scale image analysis in semantic networks. 2nd international symposium: operationalization of remote sensing.
  • Bakker W. H., Janssen L. L. F., Reeves C. V., Gorte B. G. H., Christine P., Weir M. J. C., Horn J. A., Anupma P., Tsehaie W. (2009). Principles of Remote Sensing: An Introductory Textbook. 4th Ed. ed. ITC Educational Textbook Series. Enschede, The Netherlands.
  • Baltsavias E. P. (2004). Object extraction and revision by image analysis using existing geodata and knowledge: Current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing 58:(3-4) 129-151
  • Blaschke T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65:(1) 2-16. http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004.
  • Blaschke T., Hay G. J., Kelly M., Lang S., Hofmann P., Addink E., Queiroz Feitosa R., van der Meer F., van der Werff H., van Coillie F., Tiede D. (2014). Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing 87:(0) 180-191. http://dx.doi.org/10.1016/j.isprsjprs.2013.09.014.
  • Blaschke T., Lang S., Hay G. J. (2008). Object-based image analysis : spatial concepts for knowledge-driven remote sensing applications. 1st edSpringer New York.
  • Brandtberg T., Walter F. (1998). Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications 11:(2) 64-73
  • Câmara G., Souza R. C. M., Freitas U. M., Garrido J. (1996). Spring: Integrating remote sensing and gis by object-oriented data modelling. Computers and Graphics (Pergamon) 20:(3) 395-403
  • Campbell J. B., Wynne R. H. (2012). Introduction to Remote Sensing. 5th edGuilford Publications.
  • Chubey M. S., Franklin S. E., Wulder M. A. (2006). Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing 72:(4) 383
  • Cliche G., Bonn F., Teillet P. (1985). Integration of the SPOT panchromatic channel into its multispectral mode for image sharpness enhancement. Photogrammetric Engineering and Remote Sensing 51: 311-316
  • Culvenor D. S. (2002). TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Computers & Geosciences 28:(1) 33-44
  • de Béthune S., Muller F., Donnay J.-P. (1998). Fusion of multispectral and panchromatic images by local mean and variance matching filtering techniques. Fusion of Earth Data 28-30
  • Descombes X., Pechersky E. (2006). Tree crown extraction using a three states Markov random field. INRIA.
  • Deskevich M. P., Padwick C. (2012). System and Method For Combining Color Information With Spatial Information In Multispectral Images. United States.
  • Erdin K. (1986). Fotoyorumlama ve Uzaktan Algılamaİstanbul Üniversitesi Orman Fakültesi Yayınları İstanbul.
  • Erikson M. (2003). Segmentation of individual tree crowns in colour aerial photographs using region growing supported by fuzzy rules. Canadian Journal of Forest Research 33:(8) 1557-1563
  • Garguet-Duport B., Girel J., Chassery J.-M., Patou G. (1996). The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing 62:(9) 1057-1066
  • Gougeon F. A. (1995). A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing 21:(3) 274–284
  • Gudex-Cross D., Pontius J., Adams A. (2017). Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery. Remote Sensing of Environment 196: 193-204. https://doi.org/10.1016/j.rse.2017.05.006.
  • Haralick R. M. (1983). Decision Making In Context. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5:(4) 417-428
  • Haralick R. M., Shapiro L. G. (1985). Image Segmentation Techniques. Computer Vision, Graphics, and Image Processing 29:(1) 100-132
  • Hay G., Niemann K., McLean G. (1996). An object-specific image-texture analysis of H-resolution forest imagery. Remote Sensing of Environment 55:(2) 108-122
  • Hayes D. J., Sader S. A., Schwartz N. B. (2002). Analyzing a forest conversion history database to explore the spatial and temporal characteristics of land cover change in Guatemala's Maya Biosphere Reserve. Landscape Ecology 17:(4) 299-314
  • Henrich V., Krauss G., Götze C., Sandow C. (2015). IDB - Index Database: A database for remote sensing indices.
  • Huete A., Liu H., Batchily K., Van Leeuwen W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59:(3) 440-451
  • Immitzer M., Vuolo F., Atzberger C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing 8:(3). 10.3390/rs8030166.
  • Jackson R., Slater P., Pinter Jr P. (1983). Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of Environment 13:(3) 187-208
  • Jones D., Pike S., Thomas M., Murphy D. (2011). Object-based image analysis for detection of Japanese knotweed sl taxa (Polygonaceae) in Wales (UK). Remote Sensing 3:(2) 319-342
  • Kaczynski R., Donnay J.-P., Muller F. (1995). Satellite image maps of Warsaw in the scale 1: 25,000. EARSeL Advances in Remote Sensing 4: 100-102
  • Kaufman Y. J., Tanré D. (1996). Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sensing of Environment 55:(1) 65-79
  • Ke Y., Quackenbush L. J. (2007). Forest species classification and tree crown delineation using Quickbird imagery. Proceedings of the ASPRS 2007 Annual Conference.
  • Kettig R. L., Landgrebe D. (1976). Classification of multispectral image data by extraction and classification of homogeneous objects. Geoscience Electronics, IEEE Transactions on 14:(1) 19-26
  • Koç A. (1997). Belgrad Ormanındaki Ağaç Türü Ve Karışımlarının Uydu Verileri Ve Görüntü İşleme Teknikleri İle Belirlenmesi. İ.Ü. Orman Fakültesi Dergisi A/47:(1) 89-110
  • Levine M. D., Nazif A. M. (1985). Rule-based image segmentation: a dynamic control strategy approach. Computer Vision, Graphics, & Image Processing 32:(1) 104-126
  • Li D., Ke Y., Gong H., Li X. (2015). Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images. Remote Sensing 7:(12). 10.3390/rs71215861.
  • Lillesand T., Kiefer R. W., Chipman J. (2014). Remote Sensing and Image Interpretation. 7th edWiley.Lobo A. (1997). Image segmentation and discriminant analysis for the identification of land cover units in ecology. Geoscience and Remote Sensing, IEEE Transactions on 35:(5) 1136-1145
  • Lobo A., Chic O., Casterad A. (1996). Classification of Mediterranean crops with multisensor data: Per-pixel versus per-object statistics and image segmentation. International Journal of Remote Sensing 17:(12) 2385-2400
  • McKeown Jr D. M., Harvey W. A., Wixson L. E. (1989). Automating knowledge acquisition for aerial image interpretation. Computer Vision, Graphics, & Image Processing 46:(1) 37-81
  • Navulur K. (2007). Multispectral Image Analysis Using the Object-Oriented ParadigmCRC Press/Taylor & Francis.
  • Neubert M., Meinel G. (2005). Atmospheric and terrain correction of Ikonos imagery using ATCOR3. Leibniz Institute of Ecological and Regional Development. Dresden, Germany
  • Nussbaum S. (2008). Object-based image analysis and treaty verification : new approaches in remote sensing - applied to nuclear facilities in iran. Is edSpringer Berlin Heidelberg New York, NY.
  • Padwick C., Deskevich M., Pacifici F., Smallwood S. (2010). WorldView-2 pan-sharpening. Proc. American Society for Photogrammetry and Remote Sensing 13
  • Pal N. R., Pal S. K. (1993). A review on image segmentation techniques. Pattern Recognition 26:(9) 1277-1294
  • Perrin G., Descombes X., Zerubia J. (2006). A non-Bayesian model for tree crown extraction using marked point processes. INRIA.
  • Pinz A. (1989). Final results of the vision expert system VES: finding trees in aerial photographs. Wissensbasierte Mustererkennung. Wien 49: 90-111
  • Pollock R. J. (1996). The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model. The University of British Columbia (Canada).
  • Rafieyan O., Darvishsefat A., Babaii S. (2009a). Evaluation of object-based classification method in forest applications using UltraCamD imagery (Case study: Northern forest of Iran). 3rd National Forest Congress, University of Tehran, Karaj, Iran.
  • Rafieyan O., Darvishsefat A., Babaii S., Mataji A. (2009b). Object-based classification using UltraCam-D images for tree species discrimination (Case study: Hyrcanian Forest-Iran). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38:
  • Richter R., Schläpfer D. (2014). Atmospheric/Topographic Correction For Satellite Imagery. DLR report DLR-IB. ReSe Applications Schlapfer Switzerland.
  • Rouse Jr J., Haas R., Schell J., Deering D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 351: 309
  • Ryherd S., Woodcock C. (1996). Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric Engineering and Remote Sensing 62:(2) 181-194
  • Sader S., Winne J. (1992). RGB-NDVI colour composites for visualizing forest change dynamics. International Journal of Remote Sensing 13:(16) 3055-3067
  • Sandmann H., Lertzman K. P. (2003). Combining high-resolution aerial photography with gradient-directed transects to guide field sampling and forest mapping in mountainous terrain. Forest Science 49:(3) 429-443
  • Schott J. R. (2007). Remote sensing : the image chain approach. 2nd edOxford University Press New York.
  • Sellers P. (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6:(8) 1335-1372
  • Strahler A. H., Woodcock C. E., Smith J. A. (1986). On the nature of models in remote sensing. Remote Sensing of Environment 20:(2) 121-139
  • Trimble (2012). eCognition Developer User GuideTrimble Germany GmbH München.
  • Tucker C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8:(2) 127-150
  • Updike T., Comp C. (2010). Radiometric use of WorldView-2 imagery. Technical Note 1-17
  • Welch R., Ehlers M. (1987). Merging multiresolution SPOT HRV and Landsat TM data. Photogrammetric Engineering and Remote Sensing 53: 301-303
  • Wolf A. (2010). Using WorldView 2 Vis-NIR MSI imagery to support land mapping and feature extraction using normalized difference index ratios. DigitalGlobe 8-Band Research Challenge 1-13
  • Wong T., Mansor S., Mispan M., Ahmad N., Sulaiman W. (2003). Feature extraction based on object oriented analysis. Proceedings of ATC 2003 Conference.
  • Wu B., Yu B., Wu Q., Huang Y., Chen Z., Wu J. (2016). Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. International Journal of Applied Earth Observation and Geoinformation 52: 82-94
  • Wulder M. (1998). Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography 22:(4) 449-476
  • Wulder M. A., White J. C., Hay G. J., Castilla G. (2008). Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery. The Forestry Chronicle 84:(2) 221-230. 10.5558/tfc84221-2.
  • Yan G., Mas J. F., Maathuis B., Xiangmin Z., Van Dijk P. (2006). Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing 27:(18) 4039-4055
  • Yu Q., Gong P., Clinton N., Biging G., Kelly M., Schirokauer D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing 72:(7) 799-811
  • Yurtseven H. (2014). Yüksek Çözünürlüklü Uydu Verileri ile Obje Tabanlı Görüntü Analizleri. Fen Bilimleri Enstitüsü. İstanbul, Türkiye, İstanbul Üniversitesi. 147.
  • Zhou X., Jancsó T., Chen C., Verőné M. W. (2012). Urban Land Cover Mapping Based on Object Oriented Classification Using WorldView 2 Satellite Remote Sensing Images. International Scientific Conference on Sustainable Development & Ecological Footprint, Sopron, Hungary.
Toplam 72 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Hüseyin Yurtseven 0000-0003-2469-9365

Hakan Yener 0000-0002-2136-4271

Proje Numarası 9895
Yayımlanma Tarihi 20 Ağustos 2019
Gönderilme Tarihi 7 Ağustos 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 2

Kaynak Göster

APA Yurtseven, H., & Yener, H. (2019). Using of high-resolution satellite images in object-based image analysis. Eurasian Journal of Forest Science, 7(2), 187-204. https://doi.org/10.31195/ejejfs.603510

 

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ISSN: 2147-7493

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