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The roles of textural images in improving land-cover classification in the Brazilian Amazon. Author(s): LU, D.; LI, G.; MORAN, E.; DUTRA, L.; BATISTELLA, M. Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poo... ... Repository: BDPA Publication year: 2014
Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM + and Radarsat data. Author(s): LU, D.; BATISTELLA, M.; MORAN, E. Land-cover classification with remotely sensed data in moist tropical regions in a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classificat... ... Repository: BDPA Publication year: 2007
A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Author(s): LI, G.; LU, D.; DUTRA, L.; BATISTELLA, M. This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a t... ... Repository: BDPA Publication year: 2012
Land-cover classification in the Brazilian Amazon with satélite images: towards increasing accuracy. Author(s): LU, D.; BATISTELLA, M.; MORAN, E. F. Repository: BDPA Publication year: 2006
Developments in land use and land cover classification techniques in remote sensing: a review. Author(s): MACARRINGUE, L. S.; BOLFE, E. L.; PEREIRA, P. R. M. Abstract. Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different sca... ... Repository: BDPA Publication year: 2022
Comparison of classification algorithms of images for the mapping of the land covering in Tasso Fragoso municipality, Brazil. Author(s): PEREIRA, P. R. M.; COSTA, F. W. D.; BOLFE, E. L.; MACARRINGE, L.; BOTELHO, A. C. Abstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformat... ... Repository: BDPA Publication year: 2021
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. Author(s): LU, D.; LI, G.; MORAN, E.; DUTRA, L.; BATISTELLA, M. Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This rese... ... Repository: BDPA Publication year: 2011
Regional scale land use/land cover classification using temporal series of MODIS data. Author(s): JONATHAN, M.; MEIRELLES, M. S. P.; BERROIR, J.-P.; HERLIN, I. This paper describes a methodology for systematic land use/land cover classification on a regional scale, with emphasis on a low cost and highly automatized approach. This methodology is based on mult... ... Repository: BDPA Publication year: 2006
Land use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning. Author(s): MACARRINGUE, L. S.; BOLFE, E. L.; DUVERGER, S. G.; SANO, E. E.; CALDAS, M. M.; FERREIRA, M. C.; ZULLO JUNIOR, J.; MATIAS, L. F. Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique betwe... ... Repository: BDPA Publication year: 2023
Land use/land cover classification and scale effect analysis for a multi-temporal superpixel-based segmentation using PlanetScope data. Author(s): BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. This study evaluated the accuracy and suitability of LULC classifications based on the scale effect of a multi-temporal superpixel-based segmentation using PlanetScope (PS) data. Repository: BDPA Publication year: 2023

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