This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

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) …

Convolutional neural networks (CNNs) and Transformers have made impressive progress in
the field of remote sensing change detection (CD). However, both architectures have …

For hyperspectral image (HSI) change detection (CD), multiscale features are usually used
to construct the detection models. However, the existing studies only consider the multiscale …

Nonagriculturalization incidents are serious threats to local agricultural ecosystem and
global food security. Remote sensing change detection (CD) can provide an effective …

Object detection remains a pivotal aspect of remote sensing image analysis, and recent
strides in Earth observation technology coupled with convolutional neural networks (CNNs) …

Change detection (CD) aims to identify surface changes from bitemporal images. In recent
years, deep learning (DL)-based methods have made substantial breakthroughs in the field …

Change detection (CD) of remote sensing (RS) images has enjoyed remarkable success by
virtue of convolutional neural networks (CNNs) with promising discriminative capabilities …

Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

Remote sensing image change detection (CD) is done to identify desired significant
changes between bitemporal images. Given two co-registered images taken at different …