Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

By Hongruixuan Chen, Chen Wu, Bo Du, Le Wang. IEEE Transactions on Geoscience and Remote Sensing, 2020

315 citations recorded by the source corpus.

With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detection (CD). Multisource multitemporal images can provide abundant information on observed landscapes with various physical and material views, and it is exigent to develop efficient techniques to utilize these multisource data for CD. In this article, we propose a novel and general deep siamese convolutional multiple-layers recurrent neural network (RNN) (SiamCRNN) for CD in multitemporal VHR images. Superior to most VHR image CD methods, SiamCRNN can be used for both homogeneous and heterogeneous images. Integrating the merits of both convolutional neural network (CNN) and RNN, SiamCRNN consists of three subnetworks: deep siamese convolutional neural network (DSCNN), multiple-layers RNN (MRNN), and fully connected (FC) layers. The DSCNN has a flexible structure for multisource image and is able to extract spatial–spectral features from homogeneous or heterogeneous VHR image patches. The MRNN stacked by long-short term memory (LSTM) units is responsible for mapping the spatial–spectral features extracted by DSCNN into a new latent feature space and mining the change information between them. In addition, FC, the last part of SiamCRNN, is adopted to predict change probability. The experimental results in two homogeneous data sets and one challenging heterogeneous VHR images data set demonstrate that the promising performances of the proposed network outperform several state-of-the-art approaches.

Subfields: Artificial Intelligence, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/8acd12a07d7d6411f78e45efde948a5d82304c00

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongruixuan Chen
2. Qingyang Li
3. Zhen Li
4. Chengxi Han
5. Naoto Yokoya
6. Jian Song
7. Junshi Xia
8. Ruofei Zhong

In Atmospheric Science

1. Ayesha Shafique
2. Guo Cao
3. Hongruixuan Chen
4. Muhammad Aslam
5. Qingyang Li
6. Zia Khan
7. Zhen Li
8. Chengxi Han

In Media Technology

1. Ayesha Shafique
2. Guo Cao
3. Hongruixuan Chen
4. Muhammad Aslam
5. Qingyang Li
6. Zia Khan
7. Zhen Li
8. Chengxi Han

Citations in the corpus

• Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges (2020, methodology): “temporal dependency through an AI-based classifier as a temporal module [14,38,53,74].”
• ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, methodology): “…[16] 1.35 18.66 FC-Siam-Conc [16] 1.54 21.07 SiamCRNN-18 [13] 18.85 86.64 SiamCRNN-34 [13] 28.96 113.28 SiamCRNN-50 [13] 44.45 185.30 SiamCRNN-101 [13] 63.44 224.30 DSIFN [17] 35.73 329.03 SNUNet [32] 10.21 176.36 HANet [33] 2.61 70.68 CGNet [34] 33.68 329.58 T then differentiate the damage…”
• Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, methodology): “Some previous methods have shown good performance [157–159, 162 ], but there exists some challenges.”
• Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, background): “The second type is the double-branch networks with shared weights [36], [37] (i.e., the Siamese network).”