Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 713-717, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-713-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 713-717, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-713-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

CLASSIFICATION OF THE STRUCTURE OF CITIES THROUGH MID-RESOLUTION SATELLITE IMAGERY AND PATCH BASED NEURAL NETWORKS

D. Verma1, A. Jana1, and K. Ramamritham2 D. Verma et al.
  • 1Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, India
  • 2Dept. of Computer Science and Engineering, Indian Institute of Technology Bombay, India

Keywords: Convolutional Neural Networks (CNN), Autoencoders, Sentinel 2B, Indian Cities, t-SNE, Unsupervised Clustering

Abstract. The studies in the classification of the urban spatial structure have been essential in deriving insights into the land cover and the built typology which helped in the estimation of energy consumption patterns, urban density, compactness, and hierarchy of settlements. However, the analysis and comparison of the physical forms of the cities have been attempted in a piecemeal fashion where the requirement of datasets and the computation power for analysis has been a major hindrance. With the advancement in machine learning based techniques, large datasets such as satellite imagery can be studied with advanced computer vision methods. These solutions may help in studying the intricate nature of human habitats in large extents of geographical areas including various urban areas. This study utilizes smaller patches of medium resolution Sentinel-2B Imagery of ten different cities in India to explore the urban forms present in these cities. This study uses Stacked Convolutional Autoencoder (CAE) to reduce the dimensionality of satellite imagery patches and unsupervised clustering techniques such as t-SNE and K-means to study the characteristics of similar patches. On analyzing the clusters through visual exploration, similar patches are delineated and provided with corresponding labels representing urban forms. Individual clusters are then studied with respect to each city. The motive of the study is to gain insights into the different types of morphological patterns present within and among cities.