LSTATS software and its application

Proceedings of the 7th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2010

Volumn , Issue , 2010, Pages 317-324

  Tamm, Tanel ^a   Remm, Kalle ^a   Proosa, Hendrik ^a  

^a UNIVERSITY OF TARTU   (Estonia)

Author keywords

Forest remote sensing; Local statistics; Texture

Indexed keywords

APPLICATION PROGRAMS; FORESTRY; PATTERN RECOGNITION; SIGNAL PROCESSING; TEXTURES;

FOREST REMOTE SENSING; ITS APPLICATIONS; LOCAL KERNEL; LOCAL STATISTICS; TARGET LOCATION; TREE CROWNS;

REMOTE SENSING;

COMPUTER PROGRAMS; CROWNS; FORESTRY; PATTERN RECOGNITION; REMOTE SENSING; SIGNALS; STATISTICS; TEXTURE;

EID: 77954595252     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2316/p.2010.678-129     Document Type: Conference Paper

References (33)

1. P.M. Atkinson, Geostatistical regularization in remote sensing, Modelling scale in geographical information science Edited by N.J. Tate and P.M. Atkinson (West Sussex, England: John Wiley and Sons, Ltd, 2001), 237-260.
2. T. Blaschke and J. Strobl, What's wrong with pixels? Some recent developments interfacing remote sensing and GIS, GeoBIT/GIS, 6, 2001, 12-17
3. G. Sheikholeslami, A. Zhang, L. Bian, A multi-resolution content-based retrieval approach for geographic images, GeoInformatica, 3(2), 1999, 109-139.
4. R.M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, 67(5), 1979, 786-804.
5. L. Anselin, Local indicators of spatial association-LISA, Geofraphical Analysis, 27(2), 1995, 93-115.
6. N. Coops and D. Culvenor, Utilizing local variance of simulated high spatial resolution imagery to predict spatial pattern of forest stands, Remote Sensing of the Environment, 71, 2000, 248-260.
7. A.F. Rahman, J.A. Gamon, D.A. Sims and M. Schmidts, Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland, Remote Sensing of Environment, 84, 2003, 192-207.
8. E. Muinonen, M. Maltamo, H. Hyppanen, V. Vainikainen, Forest stand characteristics estimation using a most similar neighbour approach and image spatial structure information, Remote Sensing of Environment, 78, 2001, 223-228.
9. P. Couteron, R. Pelissier, E.A. Nicolini, D. Paget, Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images, Journal of Applied Ecology, 42 (6), 2005, 1121-1128.
10. S.J. Purkis, S.W. Myint, B.M. Riegl, Enhanced detection of the coral Acropora cervicornis from satellite imagery using a textural operator, Remote Sensing of Environment 101 (1), 2006, 82-94.
11. C.S. Zhang, Y. Tang, L. Luo, W.L. Xu, Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land, Environmental Pollution, 157 (11), 3083-3090.
12. D. Pullar, MapScript: A map algebra programming language incorporating neighbourhood analysis, Geolnformatica, 5 (2), 2001, 145-163.
13. P. Kilpelainen and T. Tokola, Gain to be achieved from stand delineation in LANDSAT TM image-based estimates of stand volume, Forest Ecology and Management, 124, 1998, 105-111.
14. S. Magnussen, P. Boudewyn, M. Wulder, Contextual classification of Landsat TM images to forest inventory cover types, International Journal of Remote Sensing, 25(12), 2004, 2421-2440.
15. S. Tuominen and A. Pekkarinen, Performance of different spectral and textural aerial photograph features in multi-source forest inventory, Remote Sensing of Environment, 94, 2005, 256-268.
16. M. Linder, K. Remm, H. Proosa, The application of the concept of indicative neighbourhood on Landsat ETM+ images and orthophotos using circular and annulus kernels, Headway in Spatial Data Handling. Proceedings of the 13th International Symposium on Spatial Data Handling in Montpellier France, Edited by A. Ruas, C. Gold, Springer 2008, 147-162.
17. T. Tamm and K. Remm, Estimating the parameters of forest inventory using machine learning and the reduction of remote sensing features, International Journal Of Applied Earth Observation And Geoinformation, 11 (4), 2009, 290-297.
18. K. Remm and A. Luud, Regression and point pattern models of moose distribution in relation to habitat distribution and human influence in Ida-Viru county, Estonia, Journal for Nature Conservation, 11, 2003, 197-211.
19. K. Remm, M. Linder, L. Remm, Relative density of finds for assessing similarity-based maps of orchid occurrence, Ecological Modelling, 220 (3), 2009, 294-309.
20. K. Remm and L. Remm, Similarity-based large-scale distribution mapping of orchids, Biodiversity and Conservation, 18 (6), 2009, 1629-1647.
21. J.R. Eastman, IDRISI Andes Guide to GIS and Image Processing (Worcester, MA, USA: Clark Labs, Clark University, 2006).
22. What is ArcGIS 9.3 (Redlands, CA, USA: ESRI, 2008).
23. Definiens Developer 7 User Guide (Munchen, Germany: Definiens AG, 2007).
24. F. Denis and A. Baskurt, Directional structures detection using steerable pyramid, Proceedings Of The Society Of Photo-Optical Instrumentation Engineers (Spie), Sixth International Conference On Quality Control By Artificial Vision, Edited by K.W. Tobin and F. Meriaudeau, (5132), 2003, 524-531.
25. Q. Zhang, J. Wang, X. Peng, P. Gong, P. Shi, Urban built-up land change detection with road density and spectral information from multitemporal Landsat TM data, International Journal Of Remote Sensing, 23 (15), 2002, 3057-3078.
26. S. Ando, Consistent Gradient Operators, IEEE Transactions On Pattern Analysis And Machine Intelligence, 22 (3), 2000, 252-265.
27. M. Singh and S. Singh, Image segmentation optimisation for X-ray images of airline luggage, CIHSPS 2004: Proceedings Of The 2004 IEEE International Conference On Computational Intelligence For Homeland Security And Personal Safety, 2004, 10-17.
28. S.W. Myint, Fractal approaches in texture analysis and classification of remotely sensed data: comparisons with spatial autocorrelation techniques and simple descriptive statistics, International Journal Of Remote Sensing, 24 (9), 2003, 1925-1947.
29. S.W. Myint, N.S.N. Lam, J.M. Tyler, Wavelets for urban spatial feature discrimination: Comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches, Photogrammetric Engineering And Remote Sensing, 70 (7), 2004, 803-812.
30. S. Dray, P. Legendre, P.R. Peres-Neto, Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM), Ecological Modelling, 196 (3-4), 2006, 483-493.
31. A. Lopes, R. Touzi, E. Nezry, Adaptive Speckle Filters and Scene Heterogeneity, IEEE Transactions On Geoscience And Remote Sensing, 28 (6), 1990, 992-1000.
32. B. Aiazzi, L. Alparone, S. Baronti, Information-theoretic heterogeneity measurement for SAR imagery, IEEE Transactions On Geoscience And Remote Sensing, 43 (3), 2005, 619-624.
33. J. Caviedes, F. Oberti, A new sharpness metric based on local kurtosis, edge and energy information, Signal Processing-Image Communication, 19 (2), 2004, 147-161.