Impact assessment of humanitarian demining using object-based peri-urban land cover classification and morphological building detection from VHR Worldview imagery

Abstract

This study utilised very high-resolution (VHR) imagery to monitor and evaluate the impact of humanitarian demining activities in a peri-urban environment in Afghanistan. Identifying buildings and mapping the spatial distribution of different types of land cover is of great practical significance for demining organisations, such as the HALO Trust, to assess the impact of their mine-clearance activities, by quantifying change and the growth of a population in a specific area over time. This study had two main objectives: (i) to map the post-clearance land cover, and (ii) to detect and quantify the change in the number and area of buildings. Two independent workflows were implemented and evaluated. To map land cover, this study investigated the implementation of various machine learning algorithms in object-based image classification (OBIA) of VHR satellite imagery (Worldview-1,2,3). Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, before classification was performed based on a machine learning Random Forests (RF) approach. Different parameters and spatial distribution of training samples were tested to analyse the model's performance. Further analysis determined that by using only the segments' mean value per spectral band (Red, Green, Blue), data redundancy in the training stage was eliminated. The final classified map had an overall accuracy of 90.67% and a total built-area of 643,660.28 m2 was detected in the 4.11 km2 study area. To detect and quantify buildings present in the study area, an alternative, automatic, unsupervised approach based on the morphological building index (MBI) was implemented using MATLAB. Two VHR (0.5 m) panchromatic images acquired by WorldView-1 in 2008 and 2018 were processed using a series of multi-scale and multi-directional morphological operators, before a series of post-processing thresholds were applied to refine the output. Parameters were systematically optimised for the datasets and their sensitivity analysed. By comparing the output to manually labelled reference maps, producer's and user's accuracy of 78.63/81.70% and 75.30/77.91% were attained for 2008 and 2018 imagery respectively. The built-area was found to increase from 58,705 to 611,920 m2 over the ten-year period, indicating a significant increase in the number of people resident in the area. Whilst OBIA was found to be more accurate than MBI for the 2018 pansharpened imagery, with producer's and user's accuracy of 91.00/90.60% versus 84.65/79.44% respectively, it must be considered that the MBI, an unsupervised method requiring no training, offers a fast solution if the sole objective is building detection.