End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

By Daifeng Peng, Yongjun Zhang, Haiyan Guan. Remote Sensing, 2019

754 citations recorded by the source corpus.

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

Subfields: Artificial Intelligence, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/5c1680927fdbe638976207bfbbfb006e5bcecfbc

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Citations in the corpus

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019, background): “UNet++ has since been quickly adopted by the research community, either as a strong baseline for comparison [52]–[55], or as a source of inspiration for developing newer semantic segmentation architectures [56]–[61]; it has also been utilized for multiple applications, such as segmenting objects in ”
Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, methodology): “[44] can accurately detect the change information and can also better retain the edges to avoid the aliasing effect.”
Remote Sensing Image Change Detection With Transformers (2021, background): “approach [3], [22]–[24], [29] concatenates the bitemporal images as a single input to a semantic segmentation net-”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “[74] for end-to-end change detection of VHR satellite images.”
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing (2023, positive): “Registering image pairs is commonly used for change detection in other contexts [17,52,59], and has been applied to medical imaging [5, 23].”
SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images (2020, methodology): “In [27], we make a comprehensive review of DL-based CD (DLCD) methods, which can mainly be divided into: 1) feature-based”
A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021, background): “Since this time, FCN and its variants have provided another approach to fine-grained CD [22], [25]–[27], [49].”
Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, background): “Recent success of deep learning techniques in change detection is mainly focused on supervised methods [10]–[12], which are often limited from the availability of annotated datasets.”
SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images (2021, methodology): “Unet ++ _MSOF [12] inputs concatenated images into UNet ++ backbone and uses the multiple side-outputs fusion for deep supervision.”