Land use/cover classification in the Brazilian Amazon using satellite images

Pesquisa Agropecuária Brasileira

Dengsheng Lu²  Mateus Batistella¹  Guiying Li²  Emilio Moran²  Scott Hetrick²  Corina Da Costa Freitas¹  Luciano Vieira Dutra¹  Sidnei João Siqueira Sant'anna¹ 

[1],Indiana University Anthropological Center for Training and Research Bloomington Indiana ,USA

关键词: data fusion;    multiple sensor data;    nonparametric classifiers;    texture;    fusão de dados;    dados de sensor múltiplo;    classificadores não paramétricos;    textura;   

DOI  :  10.1590/S0100-204X2012000900004

来源: SciELO

【 摘 要 】

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 classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

【 授权许可】

CC BY   
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