SAR DATA FOR LAND USE LAND COVER CLASSIFICATION IN A TROPICAL REGION WITH FREQUENT CLOUD COVER

被引：8

作者：

Prudente, V. H. R. ^{[1
,2
]}

Sanches, I. D. ^{[1
]}

Adami, M. ^{[3
]}

Skakun, S. ^{[2
,4
]}

Oldoni, L., V ^{[1
]}

Xaud, H. A. M. ^{[5
]}

Xaud, M. R. ^{[5
]}

Zhang, Y. ^{[2
]}

机构：

[1] Natl Inst Space Res, Remote Sensing Div, BR-12227010 Sao Jose Dos Campos, SP, Brazil

[2] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[3] Natl Inst Space Res, Amazon Reg Ctr, BR-66077830 Belem, Para, Brazil

[4] NASA, Goddard Space Flight Ctr, Code 619, Greenbelt, MD 20771 USA

[5] Brazilian Agr Res Corp, Embrapa Roraima, BR-69301970 Boa Vista, Parana, Brazil

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

Roraima; machine learning; radar; Sentinel-1; SENTINEL-1;

D O I：

10.1109/IGARSS39084.2020.9323404

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study aims at mapping Land Use and Land Cover (LULC) in the region of Roraima, Brazil, using time-series of Sentinel-1 Synthetic Aperture Radar (SAR) data. All available Sentinel-1 images covering the study area were used and classified using two machine learning algorithms, namely random forest and multilayer perceptron. LULC heterogeneity with the SAR process complexity makes the process challenging in distinguishing certain classes. Results show that SAR data could be used for LULC mapping, as rainforest, savannas, water, and sandbank/outcrop classes. But cannot provide accurate separation for all classes, mainly for those with similar geometrical structures, such as regeneration areas, perennial crops, and buritizais.

引用

页码：4100 / 4103

页数：4

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