Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

Abstract

This study presents a fuzzy approach, for detection of transitioned building footprints in urban area using medium resolution datasets. Multi-temporal remote sensing data sets from Landsat-8 Operational Land Imager and Sentinel-2A were used for generation of temporal indices database. The database was generated using class-based sensor independent-normalized difference vegetation index approach, with an aim to reduce spectral dimensionality of each image and maintain temporal dimensionality. The temporal indices database was subsequently used as input in Modified Possibilistic c-means classifier for transitioned building footprints extraction. The identified transitioned building locations were validated using ground samples as well as from Google images at four different test sites. For accuracy assessment, F-measure was calculated and its value was 0.75 or higher for all training and testing sites. Thus, using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques.

Validation of the Prediction of Effectiveness of Statistical Time Series Models using an Artificial Neural Network Model

“…Dicha comparación afirma que es preferible el uso de las RBF, debido a que obtiene mejores tiempos de ejecución y en cuanto a la calidad de respuesta ambas obtienen resultados similares. Sin embargo, existen varios modelos además de las RBF Y MLP, tales como Feedforward [13], Fuzzy [14] y la Red Neural Recurrente [15] por lo cual a través de la presente investigación se determinará el mejor ellos como método para validar series de tiempo.…”