Building Change Detection Based on a Gray-Level Co-Occurrence Matrix and Artificial Neural Networks

Abstract

The recovery phase following an earthquake event is essential for urban areas with a significant number of damaged buildings. A lot of changes can take place in such a landscape within the buildings’ footprints, such as total or partial collapses, debris removal and reconstruction. Remote sensing data and methodologies can considerably contribute to site monitoring. The main objective of this paper is the change detection of the building stock in the settlement of Vrissa on Lesvos Island during the recovery phase after the catastrophic earthquake of 12 June 2017, through the analysis and processing of UAV (unmanned aerial vehicle) images and the application of Artificial Neural Networks (ANNs). More specifically, change detection of the settlement’s building stock by applying an ANN on Gray-Level Co-occurrence Matrix (GLCM) texture features of orthophotomaps acquired by UAVs was performed. For the training of the ANN, a number of GLCM texture features were defined as the independent variable, while the existence or not of structural changes in the buildings were defined as the dependent variable, assigning, respectively, the values 1 or 0 (binary classification). The ANN was trained based on the Levenberg–Marquardt algorithm, and its ability to detect changes was evaluated on the basis of the buildings’ condition, as derived from the binary classification. In conclusion, the GLCM texture feature changes in conjunction with the ANN can provide satisfactory results in predicting the structural changes of buildings with an accuracy of almost 92%.

Gradient Structure Information-Guided Attention Generative Adversarial Networks for Remote Sensing Image Generation

“…GSNet is divided into three stages to extract the sample image structure tensor, and the spatial size of the structure information feature map is downsampled by a factor of 2 each time and incorporated into the corresponding position of the main structure of the generative network. The gradient block is used to obtain the gradient map of the original image of the sample without considering the gradient direction information but using a convolutional layer with a fixed kernel, as shown in Equation (1). The gradient calculation formula used is shown in Equation (2).…”

“…𝐺 = (𝑓(𝑥 + 1, 𝑦) − 𝑓(𝑥 − 1, 𝑦)), (𝑓(𝑥, 𝑦 + 1) − 𝑓(𝑥, 𝑦 − 1)) , The gradient block is used to obtain the gradient map of the original image of the sample without considering the gradient direction information but using a convolutional layer with a fixed kernel, as shown in Equation (1). The gradient calculation formula used is shown in Equation (2).…”

“…In recent years, aerospace remote sensing imaging technology has been applied to many fields, such as land and mineral resource management and monitoring, traffic and road network safety monitoring, geological disaster early warning, and national defense system construction [1][2][3]. Meanwhile, deep learning technology has also greatly promoted the research of remote sensing images in detection and classification [4].…”

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

“…The gray level co-occurrence matrix is a way to describe image texture features. Its method for extracting image texture features includes four steps: gray image, gray level quantization, feature value calculation, and texture feature image generation [29,30]. The gray level co-occurrence matrix obtains its matrix by calculating the gray level image.…”

Change Detection from Landsat-8 Images Using a Multi-Scale Convolutional Neural Network (Case Study: Sahand City) †

“…Mishra et al [5] studied land use and land cover changes in a Himalayan watershed using the maximum likelihood algorithm on Landsat-5 and Sentinel-2 images. Christaki et al [6] applied Artificial Neural Networks to detect changes in UAV images after a catastrophic earthquake, explicitly focusing on textural features. While classical algorithms primarily rely on spectral information, which may yield less accurate outcomes, they can incorporate spatial features to improve identification accuracy.…”