Despite the success of deep learning-based change detection (CD) methods, their existing
insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them …

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

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 is widely used in the field of building monitoring. In recent years, the
progress of remote sensing image technology has provided high-resolution data. However …

Deep learning has substantially pushed forward remote sensing image change detection
through extracting discriminative hierarchical features. However, as the increasingly high …

Change detection (CD) is one of the most important topics in the field of remote sensing. In
this letter, we propose an effective satellite images CD network named DifUnet++. As the …

With the development of deep learning techniques in the field of remote sensing change
detection, many change detection algorithms based on convolutional neural networks …

Remote sensing change detection (RSCD), which identifies the changed and unchanged
pixels from a registered pair of remote sensing images, has enjoyed remarkable success …

High-resolution remote sensing (RS) image change detection (CD) focuses on ground
surface changes. It has wide applications, including territorial spatial planning, urban region …

This study presents a new end-to-end change detection network, called difference-
enhancement dense-attention convolutional neural network (DDCNN), that is designed for …