Paper Info
Title
Deforestation Change Detection Using High-Resolution Multi-Temporal Xband Sar Images And Supervised Learning Classification
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.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7730355
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Remote Sensing, Change Detection, OCI, SLIC, MLP, SAR Images, Superpixel
Computer vision,Incident report,Change detection,Computer science,Synthetic aperture radar,Remote sensing,Image segmentation,Supervised learning,Multilayer perceptron,Artificial intelligence,Cluster analysis,Image resolution
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
8
6
Name
Order
Citations
PageRank
Thiago L. M. Barreto1152.98
Rafael A. S. Rosa200.34
Christian Wimmer3316.32
Joao B. Nogueira400.34
Jurandy Almeida543135.15
Fábio A. M. Cappabianco69310.89