Ms. Franciele M. Tolentino Profile

Franciele M. Tolentino

at Univ Estadual Paulista Julio de Mesquita Filho

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Proceedings Article | 9 October 2018 Paper

Temporal generalization of an artificial neural networks for land use/land cover classification

Franciele Tolentino, Maria de Lourdes B. T. Galo, Luiz Christovam, Leandro Coladello

Proceedings Volume 10790, 107901O (2018) https://doi.org/10.1117/12.2325607

KEYWORDS: Image classification, Earth observing sensors, Landsat, Artificial neural networks, Error analysis, Vegetation, Data acquisition

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This work evaluated the performance of an Artificial Neural Network (ANN) in the temporal generalization of the Land Use and Land Cover (LULC) classes in the surroundings of the Salto Grande reservoir, located in a highly urbanized region of the São Paulo State, Brazil. Landsat-8 OLI (Operational Land Imager) multispectral images acquired in 2015, 2016 and 2017 were submitted to an ANN supervised classification. The ANN was trained with the image acquired in May 2015 to recognize five types of land cover (continental waters, forest, bare soil, agricultural area and urbanized area), using a learning rate of 0.1 and momentum of 0.5. As a classifier, the Multilayer Perceptron (MLP) ANN was used and the training algorithm was the backpropagation. To estimate classifications accuracies, checkpoints were randomly selected, and the error matrix was constructed for each date. The measures used in accuracy assessment were kappa, overall accuracy and the commission and omission errors per class. The results show that the image classification of 2015, the same year as the training data, resulted in a kappa index of 0.96, while the 2016 and 2017 classifications had kappa values of 0.72 and 0.74, respectively. Therefore, the experiments carried out to LULC classification from multitemporal scenes using a single-date trained ANN indicate the ANN's generalization capability and its potential in multitemporal analyzes. In addition, the 2016 classification, however, indicates the need to add non-spectral input data, which allows separate types of coverage of the body of water and landfill to be present when similar spectral responses.

Proceedings Article | 9 October 2018 Paper

Long-term satellite image time series for the assesment of land use/cover change in the Brazilian Amazon rainforest

Luiz Christovam, Maria L.B.T. Galo, Milton Shimabukuro, Franciele Tolentino, Leandro Coladello

Proceedings Volume 10790, 107901G (2018) https://doi.org/10.1117/12.2324875

KEYWORDS: Landsat, Satellite imaging, Satellites, Remote sensing, Vegetation, Image processing

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The regular acquisition of Earth Observations by remote sensing satellites provides long-term Satellite Image Time Series (SITS). Land surface spectral variability provides the capacity for the assessment of Land Use/Cover Change (LUCC) information through SITS. As the reduction of deforestation rates is a matter of global concern, we selected a test area in the Brazilian Amazon Rainforest to assess LUCC information through long-term SITS. Top of Atmosphere reflectance images acquired from Landsat satellites between 1984 and 2017 were downloaded. A filtering process was carried out through the analysis of cloud and shadow masks. A total of 279 images were used to build a long-term Normalized Difference Vegetation Index (NDVI) SITS for every pixel. Images from across 7 years were used to identify SITS for the classes No Change, Anthropic Change, and Natural Change in order to define reference SITS. The Fast- Dynamic Time Warping (FastDTW) algorithm was used to compute the similarity between the reference SITS and the SITS to be labeled. The K-Nearest Neighbor algorithm was applied to classify the SITS based on the similarity measurements. Two different values of the FastDTW radius parameter were used to build two LUCC maps. The overall accuracies of the LUCC maps were 58.06% and 55.02%, for the radius parameter of one and 20, respectively. It was observed that atmospheric effects, clouds, cloud shadows, smoke, among other noisy agents, can modify the real SITS shape. As a result, the use of raw SITS can lead to a reduction in the accuracy of LUCC maps. Furthermore, high cloud coverage in the Brazilian Amazon Rainforest results in high-frequency irregularity in the SITS, which further reduces the accuracy of the LUCC maps. However, the study showed that the long-term NDVI SITS can describe land cover types and the classes defined despite the constraints mentioned.

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