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 …

ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …

Deep learning (DL) algorithms are considered as a methodology of choice for remote-
sensing image analysis over the past few years. Due to its effective applications, deep …

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

Quantities of multi-temporal remote sensing (RS) images create favorable conditions for
exploring the urban change in the long term. However, diverse multi-source features and …

Hyperspectral change detection (CD) can be effectively performed using deep-learning
networks. Although these approaches require qualified training samples, it is difficult to …

Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …

Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

Change detection is an important task to identify land-cover changes between the
acquisitions at different times. For synthetic aperture radar (SAR) images, inherent speckle …