Development of New Index-Based Methodology for Extraction of Built-Up Area From Landsat7 Imagery: Comparison of Performance With SVM, ANN, and Existing Indices

• Published in: IEEE transactions on geoscience and remote sensing 2021-02, Vol.59 (2), p.1592-1603
• Main Authors: Mukherjee, Amritendu, Kumar, Arjun Anil, Ramachandran, Parthasarathy
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural network (ANN); Artificial neural networks; Artificial satellites; Classification; Earth; Estimation; Image classification; Imagery; Indexes; Land cover; Landsat7; Landsat8; machine learning (ML); Neural networks; Performance enhancement; Reflectance; Remote sensing; Satellite imagery; Satellites; Spaceborne remote sensing; Spectral reflectance; Statistical analysis; Statistical methods; support vector machine (SVM); Support vector machines; threshold estimation; urban built-up extraction
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2020.2996777

By studying the spectral reflectance features of different land cover types and leveraging information of primarily "BLUE" band along with "RED" and "NIR" bands, this article seeks to introduce a new built-up index such as powered B1 built-up index (PB1BI). The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas from Landsat7 satellite images efficiently. For Landsat7 satellite imagery, classification performances of the proposed index along with support vector machine (SVM), artificial neural network (ANN), and three existing built-up indices have been examined for three study sites of 1° Latitude \times 1^\circ Longitude ( \approx 12\,100~{\mathrm {sq}}\cdot {\mathrm {km}} ) area from three diverse geographical regions in India. The computed value of the M-Statistics for PB1BI is consistently greater than 1.80, indicating a better spectral separability between built-up and nonbuilt-up classes by the index. In order to improve the performance of the built-up indices, this article has suggested a bootstrapping method for threshold estimation in addition to the existing Otsu's method for the same. It has been found that using bootstrapping method instead of Otsu's method for threshold estimation has helped to improve the classification performance of built-up indices up to 17.75% and 40.49% in terms of overall accuracy and kappa ( \kappa ) coefficient, respectively. It has been observed that for the validation set, average overall accuracy (97.45%) and kappa ( \kappa ) coefficient (0.907) of PB1BI for considered study sites are not only significantly higher than existing indices but also comparable with the same of SVM (99.10% and 0.942) and ANN (87.24% and 0.450). This article has also shown that the proposed index provides a stable performance for multitemporal analysis of the study sites and is able to capture growth in built-up region in time horizon. The classification performance of PB1BI has also been verified for Landsat8 imagery across 11 study sites from different continents around the globe, and the results show overall accuracy and \kappa