Automatic building footprint extraction and road detection from hyperspectral imagery

Abstract

Hyperspectral imagery gives details of spectral information through hundreds of spectral bands also known as dimensionality. The bands with continuous spectral information model are capable of classifying various materials of interest. The enhanced dimensionality of such data allows for major changes in data relevant information, but it also presents a challenge to traditional methods for accurate hyperspectral image analysis so-called “curse of dimensionality.” The hyperspectral images are used to identify objects on the earth’s surface. Due to a large number of spectral bands, classifying objects using hyperspectral imagery has become more challenging. Feature reduction and extraction techniques are used to address these high-dimensionality issues. However, there are various challenges dealing with data classification with performance and computational time. As a result, a technique for hyperspectral image classification based on a convolutional neural network (CNN) along with geometric transformation and polygons segmentation process has been proposed. A CNN is used to convert compressed features into high-level features that were utilized to classify objects into buildings and road networks. The main objective of this paper is to design an automated building footprint extraction and road detection from hyperspectral imagery using CNN. The polygons segmentation is used for the extraction and detection of spectral features in hyperspectral data. CNN is used to classify these extracted spectral features, such as building footprints and road detection, using different kernels. When comparing the proposed techniques with other support vector machine and fully convolutional network methods, the experimental results show that CNN provides 97% classification accuracy.