Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

Land cover change detection (LCCD) with remote sensing images is an important
application of Earth observation data because it provides insights into environmental health …

Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Change detection captures the spatial changes from multi temporal satellite images due to
manmade or natural phenomenon. It is of great importance in remote sensing, monitoring …

Fighting Earth's degradation and safeguarding the environment are subjects of topical
interest and sources of hot debate in today's society. According to the United Nations, there …

Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have
become popular in remote sensing (RS) image change detection (CD). However, CNNs …

We review both widely used methods and new techniques proposed in the recent literature.
The basic concepts, categories, open issues, and challenges related to CD in HS images …

Change detection is of great significance to Earth observations. Recently, with the
emergence of deep learning (DL), the power and feasibility of deep convolutional neural …

Semantic change detection (SCD) extends the multiclass change detection (MCD) task to
provide not only the change locations but also the detailed land-cover/land-use (LCLU) …

Hyperspectral image change detection (HSI-CD) is a challenging task that focuses on
identifying the differences between multitemporal HSIs. The recent advancement of …