Change detection is a critical task in studying the dynamics of ecosystems and human
activities using multi-temporal remote sensing images. While deep learning has shown …

Deep-learning techniques have achieved great success in remote-sensing image change
detection. Most of them are supervised techniques, which usually require large amounts of …

Change detection (CD) of remote sensing (RS) images is mushrooming up accompanied by
the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed …

Traditional change detection methods usually follow the image differencing, change feature
extraction, and classification framework, and their performance is limited by such simple …

Change detection in high resolution remote sensing images is crucial to the understanding
of land surface changes. As traditional change detection methods are not suitable for the …

Supervised deep neural networks (DNNs) have been extensively used in diverse tasks.
Generally, training such DNNs with superior performance requires a large amount of labeled …

For change detection in remote sensing, constructing a training dataset for deep learning
models is quite difficult due to the requirements of bi-temporal supervision. To overcome this …

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 …

Extracting change regions from bitemporal images is crucial to urban planning, land, and
resources survey. In the literature, many methods obtaining difference between bitemporal …

Remote-sensing (RS) Change Detection (CD) aims to detect" changes of interest" from co-
registered bi-temporal images. The performance of existing deep supervised CD methods is …