22.1.1.1 Classification for Crops, Ground Cover, Land Use, Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. Remote Sensing.

Haralick, R.M.[Robert M.], Caspall, F., and Simonett, D.S.,
Using Radar Imagery for Crop Discrimination: A Statistical and Conditional Probability Study,
RSE(1), 1970, pp. 131-142. BibRef 7000

Haralick, R.M.[Robert M.], Hlavka, C.A., Carlyle, S.M., and Yokoyama, R.,
The Discrimination of Winter Wheat Using a Growth-State Signature,
RSE(9), 1980, pp. 277-294. BibRef 8000

Haralick, R.M.[Robert M.], Hlavka, C.A., Yokoyama, R., Carlyle, S.M.,
Spectral-Temporal Classification Using Vegetation Phenology,
GeoRS(18), No. 2, April, 1980, pp. 167-174. BibRef 8004

Ince, F.[Fuat],
The application of the coalescence clustering algorithm to remotely sensed multispectral data,
PR(14), No. 1-6, 1981, pp. 121-126.
WWW Version. 0309 BibRef

Sawada, N.[Nobuo], Numagami, H.[Hideo], Shinoda, H.[Hidenori], Kidode, M.[Masatsugu], Watanabe, S.[Sadakazu],
Application of a parallel pattern processor to remote sensing,
PR(14), No. 1-6, 1981, pp. 331-343.
WWW Version. 0309 BibRef

Wharton, S.W.,
A Contextual Classification Method for Recognizing Land Use Patterns in High Resolution Remotely Sensed Data,
PR(15), No. 4, 1982, pp. 317-324.
WWW Version. BibRef 8200

Badhwar, G.D., Austin, W.W., Carnes, J.G.,
A semi-automatic technique for multitemporal classification of a given crop within a landsat scene,
PR(15), No. 3, 1982, pp. 217-230.
WWW Version. 0309 BibRef

Davis, L.S., Wang, C.Y., Xie, H.C.,
An Experiment in Multispectral, Multitemporal Crop Classification Using Relaxation Techniques,
CVGIP(23), No. 2, August 1983, pp. 227-235.
WWW Version. BibRef 8308

Lee, T., Richards, J.A.,
Piecewise Linear Classification Using Seniority Logic Committee Methods, with Application to Remote Sensing,
PR(17), No. 4, 1984, pp. 453-464.
WWW Version. 0309ISODATA Classification. BibRef

Shoshany, M., Kutiel, P., Lavee, H., Eichler, M.,
Remote-Sensing of Vegetation Cover Along A Climatological Gradient,
PandRS(49), No. 4, August 1994, pp. 2-10. BibRef 9408

Skirvin, S.M., Dryden, G.,
Classification of LANDSAT Thematic Mapper Image Data, Chiricahua National Monument, Arizona,
AIApp(11), No. 3, 1997, pp. 90-98. 9802 BibRef

Heikkonen, J., Varfis, A.,
Land Cover Land Use Classification of Urban Areas: A Remote-Sensing Approach,
PRAI(12), No. 4, June 1998, pp. 475-489. 9808 BibRef

Heikkonen, J.[Jukka], Varfis, A.[Aristide], and Kanellopoulos, I.[Ioannis],
A Method for Remote Sensing Based Classification of Urban Areas,
SCIA97(xx-yy) 9705
HTML Version. BibRef

Lobo, A.,
Image Segmentation and Discriminant-Analysis for the Identification of Land-Cover Units in Ecology,
GeoRS(35), No. 5, September 1997, pp. 1136-1145.
IEEE Top Reference. 9710 BibRef

Bischof, H.[Horst], Schneider, W.[Werner], Pinz, A.[Axel],
Multispectral Classification of Landsat Images Using Neural Networks,
GeoRS(30), No. 3, 1992, pp. 482-490. BibRef 9200

Bischof, H.[Horst], Leonardis, A.[Ales],
Finding Optimal Neural Networks for Land Use Classification,
GeoRS(36), No. 1, 1998, pp. 337-341. BibRef 9800

Stoms, D.M., Bueno, M.J., Davis, F.W., Cassidy, K.M., Driese, K.L., Kagan, J.S.,
Map Guided Classification of Regional Land Cover with Multitemporal AVHRR Data,
PhEngRS(64), No. 8, August 1998, pp. 831-838. 9808 BibRef

Kavzoglu, T., Mather, P.M.,
Pruning artificial neural networks: an example using land cover classification of multi-sensor images,
JRS(20), No. 14, September 1999, pp. 2787. BibRef 9909

Kavzoglu, T., Mather, P.M.,
The role of feature selection in artificial neural network applications,
JRS(23), No. 15, August 2002, pp. 2919-2937. 0211 BibRef

Defries, R.S., Chan, J.C.W.[Jonathan Cheung-Wai],
Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data,
RSE(74), No. 3, 2000, pp. 503-515. 0102 BibRef

Steele, B.M.[Brian M.],
Combining Multiple Classifiers. An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping,
RSE(74), No. 3, 2000, pp. 545- 556. 0102 BibRef

Ji, C.Y.,
Land-Use Classification of Remotely Sensed Data Using Kohonen Self-Organizing Feature Map Neural Networks,
PhEngRS(66), No. 12, December 2000, pp. 1451-1460. Results are compared to those of the maximum-likelihood method and of the BP neural networks. 0101 BibRef

Webb, E.L.[Edward L.], Evangelista, M.A.[Ma. Arlene], Robinson, J.A.[Julie A.],
Digital Land-Use Classification Using Space-Shuttle-Acquired Orbital Photographs: A Quantitative Comparison with Landsat TM Imagery of a Coastal Environment, Chanthaburi, Thailand,
PhEngRS(66), No. 12, December 2000, pp. 1439-1450. 0101 Evaluation, Classifiers. BibRef

Liu, X.H.[Xue-Hua], Skidmore, A.K., van Oosten, H.,
Integration of classification methods for improvement of land-cover map accuracy,
PandRS(56), No. 4, July 2002, pp. 257-268.
HTML Version. 0207 BibRef

Debeir, O.[Olivier], van den Steen, I.[Isabelle], Latinne, P.[Patrice], van Ham, P.[Philippe], Wolff, E.[Eléonore],
Textural and Contextual Land-Cover Classification Using Single and Multiple Classifier Systems,
PhEngRS(68), No. 6, June 2002, pp. 597. Improve the accuracy of land-cover clasification with textural, contextual, and multiple classifier system.
WWW Version. 0207 BibRef

Bruzzone, L., Cossu, R.,
A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps,
GeoRS(40), No. 9, September 2002, pp. 1984-1996.
IEEE Top Reference. 0212 BibRef

Hlavka, C.A., Dungan, J.L.,
Areal Estimates of Fragmented Land Cover: Effects of Pixel Size and Model-Based Corrections,
JRS(23), No. 4, February 2002, pp. 711-724. 0202 BibRef

King, R.B.,
Land cover mapping principles: a return to interpretation fundamentals,
JRS(23), No. 18, September 2002, pp. 3525-3545.
WWW Version. 0211 BibRef

Huang, C., Davis, L.S., Townshend, J.R.G.,
An assessment of support vector machines for land cover classification,
JRS(23), No. 4, February 2002, pp. 725-749. 0202 BibRef

Tatem, A.J., Lewis, H.G., Atkinson, P.M., Nixon, M.S.,
Super-resolution land cover pattern prediction using a Hopfield neural network,
RSE(79), No. 1, January 2002, pp. 1-14.
HTML Version. 0201 BibRef

Sun, W.[Wanxiao], Heidt, V., Gong, P.[Peng], Xu, G.[Gang],
Information fusion for rural land-use classification with high-resolution satellite imagery,
GeoRS(41), No. 4, April 2003, pp. 883-890.
IEEE Abstract. IEEE Top Reference. 0307 BibRef

Rogan, J.[John], Miller, J.[Jennifer], Stow, D.[Doug], Franklin, J.[Janet], Levien, L.[Lisa], Fischer, C.[Chris],
Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data,
PhEngRS(69), No. 7, July 2003, pp. 793-804. Overall accuracies of the land-cover change maps ranged between 72 percent and 92 percent, with ancillary variables playing an important discriminatory role in the most detailed level of land-cover change.
WWW Version. 0307 BibRef

Shao, G.[Guofan], We, W.[Wenchun], Wu, G.[Gang], Zhou, X.H.[Xin-Hua], Wu, J.G.[Jian-Guo],
An Explicit Index for Assessing the Accuracy of Cover-Class Areas,
PhEngRS(69), No. 8, August 2003, pp. 907-914. The accuracy of cover class areas is not strongly related to conventional classification accuracy assessment indices, but can be assessed with a new index called Relative Errors of Area (REA).
WWW Version. 0401 BibRef

Özkan, C.[Coskun], Erbek, F.S.[Filiz Sunar],
A Comparison of Activation Functions for Multispectral Landsat TM Image Classification,
PhEngRS(69), No. 11, November 2003, pp. 1225-1234. Compare linear, sigmoid, and tangent hyperbolic activation functions through the one- and two-hidden layered MLP neural network structures trained with the scaled conjugate gradient learning algorithm, and evaluate their perfornances for a multispectral Landsat TM imagery hard classification problem.
WWW Version. 0401 BibRef

Wade, T.G.[Timothy G.], Wickham, J.D.[James D.], Nash, M.S.[Maliha S.], Neale, A.C.[Anne C.], Riitters, K.H.[Kurt H.], Jones, K.B.[K. Bruce],
A Comparison of Vector and Raster GIS Methods for Calculating Landscape Metrics Used in Environmental Assessments,
PhEngRS(69), No. 12, December 2003, pp. 1399-1405. A statistical analysis of the potential impact of processing methodology on environmental assessment results is presented.
WWW Version. 0401 BibRef

Aplin, P.[Paul], Atkinson, P.M.[Peter M.],
Predicting Missing Field Boundaries to Increase Per-Field Classification Accuracy,
PhEngRS(70), No. 1, January 2004, pp. 141-150.
WWW Version. Missing field boundaries were predicted by comparing the within-field modal land-cover proportion and local variance to increase the accuracy of per-field classification. 0403 See also Super-resolution target identification from remotely sensed images using a Hopfield neural network. BibRef

Kempeneers, P., de Backer, S., Debruyn, W., Coppin, P., Scheunders, P.,
Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection,
GeoRS(43), No. 3, March 2005, pp. 610-614.
IEEE Abstract. IEEE Top Reference. 0501 BibRef

de Backer, S.[Steve], Kempeneers, P.[Pieter], Debruyn, W.[Walter], Scheunders, P.[Paul],
Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images,
ICIAR04(II: 497-503).
WWW Version. 0409 BibRef

Li, X.[Xia],
A Four-Component Efficiency Index for Assessing Land Development Using Remote Sensing and GIS,
PhEngRS(71), No. 1, January 2005, pp. 47-58. This paper derives the indicators of quantity, quality, location, and morphology to access land development based on the integration of remote sensing and GIS.
WWW Version. 0509 BibRef

Islam, Z., Metternicht, G.,
The Performance of Fuzzy Operators on Fuzzy Classification of Urban Land Covers,
PhEngRS(71), No. 1, January 2005, pp. 59-68. Evaluation of the performance of fuzzy operators for integrating fuzzy membership values associated with multiple spectral bands for mapping urban land covers.
WWW Version. 0509 BibRef

Tran, L.T.[Liem T.], Wickham, J.D.[James D.], Jarnagin, S.T.[S. Taylor], Knight, C.G.[C. Gregory],
Mapping Spatial Thematic Accuracy with Fuzzy Sets,
PhEngRS(71), No. 1, January 2005, pp. 29-36.
WWW Version. 0509 BibRef

Pearlstine, L.[Leonard], Portier, K.M.[Kenneth M.], Smith, S.E.[Scot E.],
Textural Discrimination of an Invasive Plant, Schinus terebinthifolius, from Low Altitude Aerial Digital Imagery,
PhEngRS(71), No. 3, March 2005, pp. 289-298. Texture features derived from first and second order statistics and edge components in high-resolution digital color infrared images were tested for their ability to discriminate Schinus terebinthifolius in multiple linear logistic regressions.
WWW Version. 0509 BibRef

Ramsey, III, E.[Elijah], Rangoonwala, A.[Amina],
Leaf Optical Property Changes Associated with the Occurrence of Spartina alterniflora Dieback in Coastal Louisiana Related to Remote Sensing Mapping,
PhEngRS(71), No. 3, March 2005, pp. 299-312. Determining optimal reflectance bands for detecting march impact with hyperspectral leaf optical analysis.
WWW Version. 0509 BibRef

Sohn, Y.S.[Young-Sinn], Qi, J.G.[Jia-Guo],
Mapping Detailed Biotic Communities in the Upper San Pedro Valley of Southeastern Arizona using Landsat 7 ETM+ Data and Supervised Spectral Angle Classifier,
PhEngRS(71), No. 6, June 2005, pp. 709-718. Detailed biotic communities were mapped with high accuracy using the Supervised Spectral Angle Classifier and Landsat-7 EMT+ imagery.
WWW Version. 0509 BibRef

Pozzi, F.[Francesca], Small, C.[Christopher],
Analysis of Urban Land Cover and Population Density in the United States,
PhEngRS(71), No. 6, June 2005, pp. 719-726. Analysis of population density and vegetation distribution for several cities shows a strong correspondence in cities with high population density but considerable regional variability that precludes simple spectral classifications of land cover.
WWW Version. 0509 BibRef

Li, X.Z.[Xiu-Zhen], He, H.S.[Hong S.], Bu, R.[Rencang], Wen, Q.[Qingchun], Chang, Y.[Yu], Hu, Y.[Yuanman], Li, Y.H.[Yue-Hui],
The adequacy of different landscape metrics for various landscape patterns,
PR(38), No. 12, December 2005, pp. 2626-2638.
WWW Version. 0510 BibRef

Chen, L.[Li],
Nested Hyper-Rectangle Learning Model for Remote Sensing: Land Cover Classification,
PhEngRS(71), No. 3, March 2005, pp. 333. The NHLM learning model is presented and tested with SPOT data to illustrate an efficient and accurate supervised classification method.
WWW Version. 0509 BibRef

Atkinson, P.M.[Peter M.],
Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery,
PhEngRS(71), No. 7, July 2005, pp. 839-846. A simple and efficient pixel-swapping algorithm for increasing the spatial resolution of land-cover classification from remotely sensed imagery.
WWW Version. 0509 BibRef

Sun, W., Cetin, M., Thacker, W.C., Chin, T.M., Willsky, A.S.,
Variational Approaches on Discontinuity Localization and Field Estimation in Sea Surface Temperature and Soil Moisture,
GeoRS(44), No. 2, February 2006, pp. 336-350.
WWW Version. 0602 BibRef

Fieguth, P.W., Willsky, A.S., Menemenlis, D., Wunsch, C.I.,
A general multiresolution approach to the estimation of dense fields in remote sensing,
ICIP96(II: 609-612).
WWW Version. 9610 BibRef

Herold, M., Woodcock, C., diGregorio, A., Mayaux, P., Belward, A.S., Latham, J., Schmullius, C.C.,
A Joint Initiative for Harmonization and Validation of Land Cover Datasets,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1719-1727.
WWW Version. 0606 BibRef

Mayaux, P., Eva, H., Gallego, J., Strahler, A.H., Herold, M., Agrawal, S., Naumov, S., DeMiranda, E.E., DiBella, C.M., Ordoyne, C., Kopin, I., Roy, P.S.,
Validation of the Global Land Cover 2000 Map,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1728-1739.
WWW Version. 0606 BibRef

Abuelgasim, A.A., Fernandes, R.A., Leblanc, S.G.,
Evaluation of National and Global LAI Products Derived From Optical Remote Sensing Instruments Over Canada,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1872-1884. Leaf Area Index
WWW Version. 0606 BibRef

Deng, F., Chen, J.M., Plummer, S., Chen, M., Pisek, J.,
Algorithm for Global Leaf Area Index Retrieval Using Satellite Imagery,
GeoRS(44), No. 8, August 2006, pp. 2219-2229.
WWW Version. 0608 BibRef

Chen, J.M., Deng, F., Chen, M.,
Locally Adjusted Cubic-Spline Capping for Reconstructing Seasonal Trajectories of a Satellite-Derived Surface Parameter,
GeoRS(44), No. 8, August 2006, pp. 2230-2238.
WWW Version. 0608 BibRef

Zhang, L.P.[Liang-Pei], Huang, X., Huang, B.[Bo], Li, P.X.[Ping-Xiang],
A Pixel Shape Index Coupled With Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery,
GeoRS(44), No. 10, October 2006, pp. 2950-2961.
WWW Version. 0609 BibRef

Zhao, Y.[Yindi], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang], Huang, B.[Bo],
Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features,
GeoRS(45), No. 5, May 2007, pp. 1458-1468.
WWW Version. 0704 BibRef

Zhang, L.P.[Liang-Pei], Zhao, Y.D.[Yin-Di], Huang, B.[Bo], Li, P.X.[Ping-Xiang],
Texture Feature Fusion with Neighborhood-Oscillating Tabu Search for High Resolution Image Classification,
PhEngRS(74), No. 3, March 2008, pp. 323-332.
WWW Version. 0803Neighborhood-Oscillating tabu search integrates different types of texture features to improve classifi cation performance of high-resolution imagery. BibRef

Lathrop, R.G.[Richard G.], Montesano, P.[Paul], Haag, S.[Scott],
A Multi-scale Segmentation Approach to Mapping Seagrass Habitats Using Airborne Digital Camera Imagery,
PhEngRS(72), No. 6, June 2006, pp. 665-676.
WWW Version. 0610 BibRef

Yu, Q.[Qian], Gong, P.[Peng], Clinton, N.[Nick], Biging, G.[Greg], Kelly, M.[Maggi], Schirokauer, D.[Dave],
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery,
PhEngRS(72), No. 7, July 2006, pp. 799-812.
WWW Version. 0610Object-based classification applied in vegetation mapping at alliance level with 1-meter resolution airborne imagery compared with conventional pixel-based classification. BibRef

Wu, S.S.[Shuo-Sheng], Xu, B.[Bing], Wang, L.[Le],
Urban Land-use Classification Using Variogram-based Analysis with an Aerial Photograph,
PhEngRS(72), No. 7, July 2006, pp. 813-822.
WWW Version. 0610A variogram-based texture analysis was tested for classifying detailed urban land-use classes, such as mobile home, singlefamily house, multi-family house, industrial, and commercial, from a digital color infrared aerial photograph. BibRef

Keramitsoglou, I.[Iphigenia], Sarimveis, H.[Haralambos], Kiranoudis, C.T.[Chris T.], Kontoes, C.[Charalambos], Sifakis, N.[Nicolaos], Fitoka, E.[Eleni],
The performance of pixel window algorithms in the classification of habitats using VHSR imagery,
PandRS(60), No. 4, June 2006, pp. 225-238.
WWW Version. 0610habitat classification; RBF neural networks; kernel based re-classification; support vector machines; EUNIS BibRef

Aitkenhead, M.J., Dyer, R.,
Improving Land-cover Classification Using Recognition Threshold Neural Networks,
PhEngRS(73), No. 4, April 2007, pp. 413-421.
WWW Version. 0704Improving land-cover classification from remote sensing imagery with neural networks using a threshold of recognition below which the recognition system applies additional bootstrapped information to classify pixels. BibRef

Huang, H.[Heng], Legarsky, J.[Justin], Othman, M.[Maslina],
Land-cover Classification Using Radarsat and Landsat Imagery for St. Louis, Missouri,
PhEngRS(73), No. 1, January 2007, pp. 37-44.
WWW Version. 0704An investigation of the classification accuracy of merging satellite imagery from Radarsat and Landsat missions. BibRef

Sanchez-Hernandez, C.[Carolina], Boyd, D.S.[Doreen S.], Foody, G.M.[Giles M.],
One-Class Classification for Mapping a Specific Land-Cover Class: SVDD Classification of Fenland,
GeoRS(45), No. 4, April 2007, pp. 1061-1073.
WWW Version. 0704 BibRef

Saura, S.[Santiago], Castro, S.[Sandra],
Scaling functions for landscape pattern metrics derived from remotely sensed data: Are their subpixel estimates really accurate?,
PandRS(62), No. 3, August 2007, pp. 201-216.
WWW Version. 0709Scale; Landscape pattern; Sensor spatial resolution; Spatial metrics; Landscape ecology; Land cover analysis BibRef

Lucas, R.[Richard], Rowlands, A.[Aled], Brown, A.[Alan], Keyworth, S.[Steve], Bunting, P.[Peter],
Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping,
PandRS(62), No. 3, August 2007, pp. 165-185.
WWW Version. 0709Time-series imagery; Landsat; Segmentation; Decision rules; Fuzzy membership BibRef

Yang, P., Shibasaki, R., Wu, W., Zhou, Q., Chen, Z., Zha, Y., Shi, Y., Tang, H.,
Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images,
GeoRS(45), No. 10, October 2007, pp. 3087-3097.
WWW Version. 0711 BibRef

Makido, Y.[Yasuyo], Shortridge, A.[Ashton],
Weighting Function Alternatives for a Subpixel Allocation Model,
PhEngRS(73), No. 11, November 2007, pp. 1233-1240.
WWW Version. 0709Properties of a pixel-swapping optimization algorithm for predicting subpixel land-cover distribution are investigated, and improvements to it are evaluated. BibRef

Van de Voorde, T.[Tim], De Genst, W.[William], Canters, F.[Frank],
Improving Pixel-based VHR Land-cover Classifications of Urban Areas with Post-classification Techniques,
PhEngRS(73), No. 9, September 2007, pp. 1017-1028.
WWW Version. 0709Three post-classification techniques were applied to improve the accuracy and the structural coherence of an urban land-cover map derived from a soft pixel-based classification. BibRef

Xu, B.[Bing], Gong, P.[Peng],
Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data,
PhEngRS(73), No. 8, August 2007, pp. 955-965.
WWW Version. 0709Land-use and land-cover classification in an urban rural fringe of the San Francisco Bay Area using EO-1 Hyperion imagery is compared with that using EO-1 ALI imagery, and the application of a computationally efficient segmentation-based feature reduction approach. BibRef

Makido, Y.[Yasuyo], Shortridge, A.[Ashton], Messina, J.P.[Joseph P.],
Assessing Alternatives for Modeling the Spatial Distribution of Multiple Land-cover Classes at Sub-pixel Scales,
PhEngRS(73), No. 8, August 2007, pp. 935-944.
WWW Version. 0709Evaluating three methods for modeling the spatial distribution of multiple land cover classes at sub-pixel scales. BibRef

Budreski, K.A.[Katherine A.], Wynne, R.H.[Randolph H.], Browder, J.O.[John O.], Campbell, J.B.[James B.],
Comparison of Segment and Pixel-based Non-parametric Land Cover Classification in the Brazilian Amazon Using Multi-temporal Landsat TM/ETM+ Imagery,
PhEngRS(73), No. 7, July 2007, pp. 813-828.
WWW Version. 0709Accurate land-cover maps were produced using inter-annual, multi-temporal Landsat TM/EMT+ imagery and pixel-based kNN and CART®; segmentation proved unnecessary. BibRef

Addink, E.A.[Elisabeth A.], de Jong, S.M.[Steven M.], Pebesma, E.J.[Edzer J.],
The Importance of Scale in Object-based Mapping of Vegetation Parameters with Hyperspectral Imagery,
PhEngRS(73), No. 8, August 2007, pp. 905-912.
WWW Version. 0709An investigation of optimal object definition for prediction of biomass and leaf area index. BibRef

Mahtab, A., Sridhar, V.N., Navalgund, R.R.,
Impact of Surface Anisotropy on Classification Accuracy of Selected Vegetation Classes: An Evaluation Using Multidate Multiangular MISR Data Over Parts of Madhya Pradesh, India,
GeoRS(46), No. 1, January 2008, pp. 250-258.
WWW Version. 0712 BibRef

Myint, S.W.[Soe W.], Wentz, E.A.[Elizabeth A.], Purkis, S.J.[Sam J.],
Employing Spatial Metrics in Urban Land-use/Landcover Mapping: Comparing the Getis and Geary Indices,
PhEngRS(73), No. 12, December 2007, pp. 1403-1417.
WWW Version. 0712The effectiveness of Getis index (Gi) in comparison to a measure of spatial autocorrelation (Geary's C) in classifying landuse / land-cover classes in a high resolution imagery and the impact of distance threshold used in Getis index with regards to the classification accuracy. BibRef

Bagan, H.[Hasi], Wang, Q.X.[Qin-Xue], Watanabe, M.[Masataka], Kameyama, S.[Satoshi], Bao, Y.H.[Yu-Hai],
Land-cover Classification Using ASTER Multi-band Combinations Based on Wavelet Fusion and SOM Neural Network,
PhEngRS(74), No. 3, March 2008, pp. 333-342.
WWW Version. 0803A land-cover classification methodology using ASTER VNIR, SWIR, and TIR band combinations based on wavelet fusion and SOM neural network methods, and classification accuracy of different band combinations. BibRef

Chastain Jr., R.A.[Robert A.], Struckhoff, M.A.[Matthew A.], He, H.[Hong], Larsen, D.R.[David R.],
Mapping Vegetation Communities Using Statistical Data Fusion in the Ozark National Scenic Riverways, Missouri, USA,
PhEngRS(74), No. 2, February 2008, pp. 247-264.
WWW Version. 0803A vegetation community map was produced for the Ozark National Scenic Riverways using a discriminant analysis statistical approach combined with photointerpretation to exploit a large set of input variables obtained from remote sensing and topographic data. BibRef

Trias-Sanz, R.[Roger], Stamon, G.[Georges], Louchet, J.[Jean],
Using colour, texture, and hierarchial segmentation for high-resolution remote sensing,
PandRS(63), No. 2, March 2008, pp. 156-168.
WWW Version. 0803Segmentation; Hierarchical; Colour; Cartography; Land cover BibRef

Tseng, M.H.[Ming-Hseng], Chen, S.J.[Sheng-Jhe], Hwang, G.H.[Gwo-Haur], Shen, M.Y.[Ming-Yu],
A genetic algorithm rule-based approach for land-cover classification,
PandRS(63), No. 2, March 2008, pp. 202-212.
WWW Version. 0803Classification; Land-cover; Rule-based; Genetic algorithm; Knowledge rules BibRef

Karjalainen, M.[Mika], Kaartinen, H.[Harri], Hyyppä, J.[Juha],
Agricultural Monitoring Using Envisat Alternating Polarization SAR Images,
PhEngRS(74), No. 1, January 2008, pp. 117-128
WWW Version. 0803Satellite images will improve yield estimation in the future because they can provide objective information about crop growth over large areas; in this context SAR images are extremely useful due to their high revisit imaging capability. BibRef

Chen, D.M.[Dong-Mei],
A Standardized Probability Comparison Approach for Evaluating and Combining Pixel-based Classification Procedures,
PhEngRS(74), No. 5, May 2008, pp. 601-610.
WWW Version. 0804An objective approach to evaluate pixel labeling confidence in a classification and to combine classified maps generated from different classification procedures. BibRef

Mitrakis, N.E., Topaloglou, C.A., Alexandridis, T.K., Theocharis, J.B., Zalidis, G.C.,
Decision Fusion of GA Self-Organizing Neuro-Fuzzy Multilayered Classifiers for Land Cover Classification Using Textural and Spectral Features,
GeoRS(46), No. 7, July 2008, pp. 2137-2152.
WWW Version. 0806 BibRef


Silva, S.[Sara], Tseng, Y.T.[Yao-Ting],
Classification of Seafloor Habitats Using Genetic Programming,
EvoIASP08(xx-yy).
WWW Version. 0804 BibRef

Alonso, M.C.[María C.], Sanz, M.A.[María A.], Malpica, J.A.[José A.],
Classification of High Resolution Satellite Images Using Texture from the Panchromatic Band,
ISVC07(II: 499-508).
WWW Version. 0711 BibRef

Le Bris, A., Boldo, D.,
Extraction of Landcover Themes out of Aerial Orthoimages in Mountainous Areas Using External Information,
PIA07(123).
PDF Version. 0711 BibRef

Helmholz, P., Gerke, M., Heipke, C.,
Automatic Discrimination of Farmland Types Using IKONOS Imagery,
PIA07(81).
PDF Version. 0711 BibRef

Brzank, A., Heipke, C.,
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Chmiel, J.,
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Delenne, C., Rabatel, G., Agurto, V., Deshayes, M.,
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Tarantino, E., Caprioli, M.,
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Preiner, M., Weinke, E., Lang, S.,
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Urbanski, J.,
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Luscier, J.D., Thompson, W.L., Wilson, J.M., Gorham, B.E., Dragut, L.D.,
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Mavrantza, O.D., Argialas, D.P.,
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Müterthies, M., Buck, O.,
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Riedel, T., Thiel, C., Schmullius, C.,
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Levick, S., Rogers, K.H.,
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Lübker, T., Schaab, G.,
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Grenier, M., Demers, A.M., Labrecque, S., Benoit, M., Fournier, R., Drolet, B.,
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Kelly, M., Tuxen, K.,
Integrating Lidar and CIR imagery for mapping tidal wetlands: an object-based approach,
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Yokota, S., Takeuchi, K.,
Study on the relationship between landscape characteristics of fragmented urban green spaces and distribution of urban butterflies - Application of object-based satellite image analysis,
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Kux, H., Araújo, E.,
Multi-temporal object-oriented classifications and analysis of Quickbird scenes at a metropolitan area in Brazil (Belo Horizonte, Minas Gerais State),
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Kux, H., Pinho, C.,
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Pan, C.[Chunhong], Wu, G.[Gang], Prinet, V.[Veronique], Yang, Q.[Qing], Ma, S.[Songde],
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Taniguchi, R.I., Kawaguchi, E.,
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Last update:Jun 25, 2008 at 13:37:57