ICT - Outras produções

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https://hdl.handle.net/11600/61395

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Navegando ICT - Outras produções por Autor "Barreto, Thiago L. M."

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    BMINSAR: A NOVEL APPROACH FOR INSAR PHASE DENOISING BY CLUSTERING AND BLOCK MATCHING
    (IEEE, 2017) Barreto, Thiago L. M.; Rosa, Rafael A. S.; Wimmer, Christian; Moreira, Joao R.; Bins, Leonardo S.; Almeida, Jurandy [UNIFESP]; Cappabianco, Fabio A. M. [UNIFESP]
    We present a novel approach for phase denoising in Interferometric Synthetic Aperture Radar (InSAR) images, named as Block-Matching InSAR (BMInSAR). It uses k-means clustering to solve the block matching similarity search problem, thus simplifying preprocessing steps and filtering several reference-blocks at once. Also, we propose a novel methodology based on ground-truth GPS measurements to assess the filtering quality of Digital Elevation Models (DEMs) derived from a pair of Very High-Resolution (VHR) SAR complex images. Our dataset was obtained by X-Band airborne sensor OrbiSAR-2 from BRADAR. BMInSAR significantly outperforms the state-of-the-art filtering methods in both accuracy and execution time. After filtering with BMInSAR, we achieved an accuracy of 21 cm in the resulting DEM of a homogeneous lawn area, which is quite similar to that obtained by LiDAR technology.
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    Deforestation change detection using high-resolution multi-temporal xband sar images and supervised learning classification
    (Amer Inst Physics, 2016) Barreto, Thiago L. M.; Rosa, Rafael A. S.; Wimmer, Christian; Nogueira, Joao B., Jr.; Almeida, Jurandy [UNIFESP]; Menocci Cappabianco, Fabio Augusto [UNIFESP]
    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.