Grey level co-occurrence matrix

The Grey Level Co-occurrence Matrix (GLCM), introduced by Haralick et al. in 1973, is a texture analysis technique. Its core definition is a 2D matrix that tabulates the frequency of co-occurring grey-level value pairs of pixels at a specific distance and orientation. Analyzing this matrix yields texture features like contrast, correlation, energy, and homogeneity. Within risk management frameworks, GLCM is not directly defined by standards like ISO 31000 or ISO 22301. Instead, it serves as an advanced tool for implementing their requirements. For instance, when conducting a Business Impact Analysis (BIA) under ISO 22301:2019, an enterprise can use GLCM to analyze pre- and post-disaster imagery to precisely quantify physical impacts. Unlike manual visual inspection, GLCM provides objective, repeatable, and quantifiable data on textural changes, thereby enhancing the accuracy of risk assessments.

In enterprise risk management, particularly Business Continuity Management (BCM), GLCM is primarily used for Post-Disaster Damage Assessment. The implementation involves three key steps. Step 1: Data Acquisition and Calibration: Obtain high-resolution satellite or drone imagery of the same area before and after a disaster, performing radiometric and geometric corrections to ensure comparability. Step 2: Texture Feature Extraction: Calculate the GLCM for both images and extract key texture features. For example, a collapsed building will significantly decrease texture homogeneity and increase contrast. Step 3: Change Detection and Impact Quantification: Compare the pre- and post-disaster texture feature maps to identify areas of significant change and overlay them with the company's asset map (GIS). A semiconductor company could use this to assess facility damage after a typhoon, reducing assessment time from a week to a day and providing objective evidence for insurance claims with over 95% accuracy.

Taiwan enterprises face three main challenges in adopting GLCM. First, the cost and timeliness of high-resolution imagery: commercial satellite data is expensive and can be delayed by bad weather. The solution is a hybrid acquisition strategy, partnering with public agencies like TASA and maintaining an in-house drone response team. Second, a shortage of interdisciplinary talent: the technique requires experts in remote sensing, machine learning, and risk management. This can be mitigated by collaborating with specialized consulting firms like Winners Consulting for project implementation and internal training. Third, the complexity of model validation and system integration: integrating analysis results with existing GIS or ERP systems and validating them with ground-truthing is technically demanding. The recommended approach is to start with a pilot project at a single critical site to prove the concept before a full-scale rollout.

Winners Consulting specializes in Grey level co-occurrence matrix for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact