GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection
By Qi Wang, Zhenghang Yuan, Qian Du, Xuelong Li. IEEE Transactions on Geoscience and Remote Sensing, 2019
487 citations recorded by the source corpus.
Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of the hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high-dimension problem and explore abundance information, this paper presents a general end-to-end 2-D convolutional neural network (CNN) framework for hyperspectral image CD (HSI-CD). The main contributions of this paper are threefold: 1) mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multisource information; 2) 2-D CNN is designed to learn the discriminative features effectively from the multisource data at a higher level and enhance the generalization ability of the proposed CD algorithm; and 3) the new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate that the proposed method outperforms most of the state of the arts.
Subfields: Artificial Intelligence, Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/d374b35a034719501c352187dc4f8c9d9508139a
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
- Daifeng Peng
- Yongjun Zhang
- Alberto Signoroni
- Xiaoping Liu
- Haiyan Guan
- Annalisa Baronio
- Mattia Savardi
- Sergio Benini
In Ecology
- Alberto Signoroni
- Xiaoping Liu
- Fei Wang
- Mengxi Liu
- Shengchen Li
- Qian Shi
- Annalisa Baronio
- Mattia Savardi
In Media Technology
- Le Wang
- Alberto Signoroni
- Annalisa Baronio
- Mattia Savardi
- Sergio Benini
- Ayesha Shafique
- Guo Cao
- Daifeng Peng
Citations in the corpus
- A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021, methodology): “In addition to multi-spectral images, some hyperspectral image-based CDDs have also been proposed in recent years, including the hyper-spectral change detection dataset (HCCD) [38] and “River” dataset [39].”
- Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, methodology): “Wang et al. [155] provided a GETNET network for an HS image CD.”
- Deep learning for change detection in remote sensing: a review (2022, background): “Among these 22 studies, 20 worked on farmland (Wang et al. 2019; Li, Yuan, and Wang 2019; Yuan, Wang, and Li 2018) and the other two on forests (Khan et al. 2017).”
- End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++ (2019, methodology): “[45] performed hyperspectral image CD by a general end-to-end 2-D CNN framework, where 2-D mixed-affinity matrices are generated and pixel change types are obtained by the CNN output.”
- Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, methodology): “Similar to them, in [74], a method exploiting change detection was described.”