Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

Change detection based on remote sensing (RS) data is an important method of detecting
changes on the Earth's surface and has a wide range of applications in urban planning …

Change detection plays a crucial role in observing earth surface transition and has been
widely investigated using deep learning methods. However, the current deep learning …

In this paper, we propose a context-sensitive technique for unsupervised change detection
in multitemporal remote sensing images. The technique is based on fuzzy clustering …

Ecological data are considered to be difficult to analyze because numerous biological and
environmental factors are involved in a complex manner in environment–organism …

Hyperspectral (HS) images provide a dense sampling of target spectral signatures. Thus,
they can be used in a multitemporal framework to detect and discriminate between different …

Remotely sensed high-resolution satellite images contain various information in context of
changes. By analyzing this information very minutely, changes occurred in various …

Economic development and population growth have triggered rapid changes to Earth's land
cover over the last two centuries, and there is every indication that the pace of these …

The change-detection problem can be viewed as an unsupervised classification problem
with two classes corresponding to changed and unchanged areas. Image differencing is a …

In this paper we have used two fuzzy clustering algorithms, namely fuzzy c-means (FCM)
and Gustafson–Kessel clustering (GKC) along with local information for unsupervised …