Yu Li

TL;DR: Experimental results indicated that feature normalization is not necessarily significant depending on fusion methods, however, experiments showed a fluctuation in classification accuracy using an ANN with normalized features, so more experiments are required to investigate the optimal normalization approaches for the optical and SAR images when using anANN as the fusion method.

TL;DR: Deep learning algorithms such as the stacked autoencoder (SAE) and deep belief network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training to show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM) and traditional artificial neural networks with similar parameter settings.

TL;DR: Wang et al. as discussed by the authors presented surface urban heat island (SUHI) analysis of Shanghai (China) based on the change in land use and land cover using satellite Landsat images from 2002 to 2013.

TL;DR: The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future and it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China.

TL;DR: In this article, a comparative study on the combined use of multispectral optical data and dual-polarimetric SAR data to identify urban impervious surfaces is presented.