Deep Siamese Networks Based Change Detection with Remote Sensing Images

Yang, Le; Chen, Yiming; Song, Shiji; Li, Fan; Huang, Gao

doi:10.3390/rs13173394

Cited by 40 publications

(14 citation statements)

References 49 publications

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“…The proposed network uses ResNet18 as its backbone network. Based on some previous work [ 42 , 50 , 51 ], the proposed network modifies Res-Net18 by removing the last max-pooling layer and the fully connected layer and retaining the layers in the first five convolutional blocks (Conv1 to Conv5).…”

Section: Methodsmentioning

confidence: 99%

MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images

Shi

et al. 2022

Sensors

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One of the most important tasks in remote sensing image analysis is remote sensing image Change Detection (CD), and CD is the key to helping people obtain more accurate information about changes on the Earth’s surface. A Multi-Attention Guided Feature Fusion Network (MAFF-Net) for CD tasks has been designed. The network enhances feature extraction and feature fusion by building different blocks. First, a Feature Enhancement Module (FEM) is proposed. The FEM introduces Coordinate Attention (CA). The CA block embeds the position information into the channel attention to obtain the accurate position information and channel relationships of the remote sensing images. An updated feature map is obtained by using an element-wise summation of the input of the FEM and the output of the CA. The FEM enhances the feature representation in the network. Then, an attention-based Feature Fusion Module (FFM) is designed. It changes the previous idea of layer-by-layer fusion and chooses cross-layer aggregation. The FFM is to compensate for some semantic information missing as the number of layers increases. FFM plays an important role in the communication of feature maps at different scales. To further refine the feature representation, a Refinement Residual Block (RRB) is proposed. The RRB changes the number of channels of the aggregated features and uses convolutional blocks to further refine the feature representation. Compared with all compared methods, MAFF-Net improves the F1-Score scores by 4.9%, 3.2%, and 1.7% on three publicly available benchmark datasets, the CDD, LEVIR-CD, and WHU-CD datasets, respectively. The experimental results show that MAFF-Net achieves state-of-the-art (SOTA) CD performance on these three challenging datasets.

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Section: Methodsmentioning

confidence: 99%

MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images

Shi

et al. 2022

Sensors

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“…There are also Siamese networks that do work in the image processing domain, such as [25], but they focus on change detection as a binary segmentation, suitable for tracking single cells, but not for the regression task at hand. Ref.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis

Stallmann

Hammer

2023

Algorithms

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Novel neural network models that can handle complex tasks with fewer examples than before are being developed for a wide range of applications. In some fields, even the creation of a few labels is a laborious task and impractical, especially for data that require more than a few seconds to generate each label. In the biotechnological domain, cell cultivation experiments are usually done by varying the circumstances of the experiments, seldom in such a way that hand-labeled data of one experiment cannot be used in others. In this field, exact cell counts are required for analysis, and even by modern standards, semi-supervised models typically need hundreds of labels to achieve acceptable accuracy on this task, while classical image processing yields unsatisfactory results. We research whether an unsupervised learning scheme is able to accomplish this task without manual labeling of the given data. We present a VAE-based Siamese architecture that is expanded in a cyclic fashion to allow the use of labeled synthetic data. In particular, we focus on generating pseudo-natural images from synthetic images for which the target variable is known to mimic the existence of labeled natural data. We show that this learning scheme provides reliable estimates for multiple microscopy technologies and for unseen data sets without manual labeling. We provide the source code as well as the data we use. The code package is open source and free to use (MIT licensed).

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“…To compare the performance of the method proposed here, two state-of-the-art deeplearning methods were selected for this study. The first method was the deep Siamese network, which has been proposed in many studies for change detection purposes [71][72][73]. This method has three convolution layers in each stream, and then fully connected was used for classification.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021

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Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

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Deep Siamese Networks Based Change Detection with Remote Sensing Images

Cited by 40 publications

References 49 publications

MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images

MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images

Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

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