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High-level feature extraction and hierarchical feature representation of image objects with a convolutional neural network (CNN) can overcome the limitations of the traditional building detection models using middle/low-level features extracted from a complex background. Aiming at the drawbacks of manual village location, high cost, and the limited accuracy of building detection in the existing rural...
Traditional monitoring method of PM2.5 concentrations with field campaigns cannot accurately identify the spatial pattern of air pollution in urban areas. Remote sensing techniques have been applied to monitor the distribution of atmospheric particulate pollution. However, remotely sensed aerosol data products with low spatial-resolution cannot reveal the spatial variations of urban air pollution...
In this letter, we present a novel approach for spectral-spatial classification in hyperspectral imagery. To this end, after applying principal component analysis (PCA) for dimensionality reduction, we extract the spectral-spatial information by first reorganizing the local image patch with the first d principal components(PCs) into a vector representation, followed by a sorting scheme to make it...
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