Automatic extraction of built-up areas utilizing built-up indices has increased attention due to their use in monitoring urban growth and environment related problems. In this research study, a new Modified Built-up Index (MBI) is proposed utilizing Landsat 8 Imagery. This technique is developed by combining advanced slope based indexing technique built-up index (ASIT_BI), normalized difference water index (NDWI), soil adjusted vegetation index (SAVI) and brightness temperature (T_B). For this study, the optimal threshold values are obtained using Otsu’s thresholding method and modified two-stage multi-threshold Otsu (TSMO) method. Built-up area mapping/extraction using MBI is also compared with two other built-up indices namely ASIT_BI and MNDISI in term of accuracy assessment using recent Landsat 8 images. The results indicate that built-up indices are greatly affected by seasonality of image. The proposed Modified Built-up Index (MBI) results in highest mapping accuracy for all the four seasons with highest overall agreement of 97.8% for spring season. The proposed MBI method is more effective than previous built-up indexing methods because of the improved ability to separate built-up land from barren land.

利用建筑物指数自动提取建筑物区域，已引起人们的关注，因为它们被用于监测城市增长和与环境有关的问题。在这项研究中，利用Landsat 8影像提出了一种新的改良建筑指数（MBI）。这种技术是通过将先进的基于斜率索引技术建成指数（ASIT_BI），归一化差异水指数（NDWI），土壤调节植被指数（SAVI）和亮度温度（T开发_乙）。对于本研究，使用Otsu的阈值方法和改进的两阶段多阈值Otsu（TSMO）方法获得最佳阈值。在使用最近的Landsat 8图像进行准确性评估方面，还将使用MBI的建筑面积映射/提取与其他两个建筑指数ASIT_BI和MNDISI进行了比较。结果表明，堆积指数受图像季节性的影响很大。拟议的改良建筑指数（MBI）使得所有四个季节的制图精度最高，春季的总体一致性最高，为97.8％。提出的MBI方法比以前的建筑物索引方法更有效，因为改进了将土地与贫瘠土地分离的能力。