Deforestation change detection using high-resolution multi-temporal xband sar images and supervised learning classification

Deforestation change detection using high-resolution multi-temporal xband sar images and supervised learning classification

Author Barreto, Thiago L. M. Google Scholar
Rosa, Rafael A. S. Google Scholar
Wimmer, Christian Google Scholar
Nogueira, Joao B., Jr. Google Scholar
Almeida, Jurandy Autor UNIFESP Google Scholar
Menocci Cappabianco, Fabio Augusto Autor UNIFESP Google Scholar
Abstract Remote sensing has been widely applied for environmental monitoring by means of change detection techniques, commonly for identifying deforestation signs which is the gateway for illegal activities such as uncontrolled urban growth and grazing pasture. Monthly acquired X-Band images from airborne Synthetic Aperture Radar (SAR) provided multi-temporal scenes employed in this work resulting in environmental incident reports forwarded to the responsible authorities. The present work proposes the use of both, Superpixel segmentation by Simple Linear Iterative Clustering (SLIC) and change detection by Object Correlation Images (OCI) not yet applied to multi-temporal X-Band high resolution SAR images, and the application of a simple Multilayer Perceptron (MLP) supervised learning technique for detecting and classifying the changes into relevant activities. Experiments have been performed using acquired SAR imagery from BRADAR airborne sensor OrbiSAR-2 under Brazilian Atlantic Forest which revealed possible deforestation activities comparing achieved results with those obtained with experts.
Keywords Remote Sensing
Change Detection
Oci
Slic
Mlp
Sar Images
Superpixel
Language English
Date 2016
Published in 2016 Ieee International Geoscience And Remote Sensing Symposium (IGARSS). New york, p. 5201-5204, 2016.
ISSN 2153-6996 (Sherpa/Romeo, impact factor)
Publisher Amer Inst Physics
Extent 5201-5204
Origin http://dx.doi.org/10.1109/IGARSS.2016.7730355
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000388114605031
URI http://repositorio.unifesp.br/handle/11600/49266

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