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| Lu,Dengsheng; Batistella,Mateus. |
| Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Texture; Aboveground biomass; TM image; Correlation; Amazon. |
| Ano: 2005 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672005000200015 |
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| Lu,Dengsheng; Batistella,Mateus; Li,Guiying; Moran,Emilio; Hetrick,Scott; Freitas,Corina da Costa; Dutra,Luciano Vieira; Sant'Anna,Sidnei João Siqueira. |
| Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Data fusion; Multiple sensor data; Nonparametric classifiers; Texture. |
| Ano: 2012 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2012000900004 |
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