Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

Abstract

Deep learning (DL) algorithms are considered as a methodology of choice for remotesensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which are frequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research.

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

“…The feature maps of various sizes are evaluated for the recognition of small objects. Therefore, it is able to recognize relatively large-sized targets in an image [25,[30][31][32]. Finally, predictions of bounding boxes for each cell on the feature map are carried out in the network output using Equations (5)- (8), where the center coordinates and size of the obtained bounding box are described by Bx, By, Ba, Bb, respectively, as seen in Figure 5.…”

Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

“…Currently, a lot of change detection methods have been reported to detect the changed information on this earth we live. These change detection methods can be roughly grouped into three categories: pixel-based approaches [8][9][10][11][12][13][14][15][16][17][18][19], objectbased approaches [20][21][22][23][24][25], and deep learning (DL) based approaches [26][27][28][29][30][31][32][33][34].…”

“…Driven by the complex image data and wide application of change detection, many DL-based approaches [26][27][28][29][30][31][32][33][34] have been widely used for change detection tasks since they show powerful ability on feature mining. Generally, change detection concerns three types of images, i.e., Synthetic Aperture Radar (SAR) images, VHR images, and hyperspectral images.…”

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

“…With the progress and development of machine learning theory, various machine learning techniques such as support vector machine (SVM) [37][38][39][40][41] and random forest (RF) [42][43][44][45][46] have been applied for change detection. In recent years, with the rapid development of big data and artificial intelligence technology, deep learning methods such as deep belief networks, convolutional neural network and twin network [47][48][49][50][51][52] has been applied to change detection, which improves the accuracy of change detection greatly. There are two types of change detection by supervision.…”