research-article

Dual Partial Recurrent Networks for Hyperspectral Image Change Detection

Authors:

Jinlong LiAuthors Info & Claims

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 70, Pages 1 - 7

https://doi.org/10.1145/3508546.3508616

Published: 25 February 2022 Publication History

Abstract

This paper presents a Dual Partial Recurrent Networks (DUAL-PRNs) which can project more accurate and effective image features by learning invariant pixel pairs with high confidence. The Change Vector Analysis provides a reference for the model to select invariant pixel pairs with high confidence as training samples. Then, the Unsupervised Slow Feature Analysis (USFA) is utilized to suppress the invariant pixel features projected by DUAL-PRNs, and highlight the variant pixel features, respectively. Thus, more obvious discrimination between the invariant and variant pixels can be achieved. Two groups of features are then obtained by passing bi-temporal remote sensing images through DUAL-PRNs and USFA. Chi-square distance is employed to calculate the divergence between two groups of features and thus generate the Change Intensity Map. Finally, the thresholding algorithm transforms the change intensity map into binary change map. Experimental results show that the proposed change detection model DUAL-PRNs performs better than the advanced model DSFA-128-2.

References

[1]

Zhou, H., Liu, S., He, J., Wen, Q., Song, L., and Ma, Y.: ‘A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features’, International Journal of Remote Sensing, 2016, 37, (19), pp. 4554-4573

Digital Library

[2]

Schott, J.R., Salvaggio, C., and Volchok, W.J.: ‘Radiometric scene normalization using pseudoinvariant features’, Remote sensing of Environment, 1988, 26, (1), pp. 1-16

[3]

Cheng, X.-y., Zhuang, X.-q., Zhang, D., Yao, Y., Hou, J., He, D.-g., Jia, J.-x., and Wang, Y.-m.: ‘A relative radiometric correction method for airborne SWIR hyperspectral image using the side-slither technique’, Optical and Quantum Electronics, 2019, 51, (4), pp. 1-16

[4]

Li, Z., Shen, H., Cheng, Q., Li, W., and Zhang, L.: ‘Thick cloud removal in high-resolution satellite images using stepwise radiometric adjustment and residual correction’, Remote Sensing, 2019, 11, (16), pp. 1925

[5]

Klampfl, S., and Maass, W.: ‘Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks’, Advances in Neural Information Processing Systems, 2009, 22, pp. 988-996

[6]

Wu, C., Du, B., Cui, X., and Zhang, L.: ‘A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion’, Remote Sensing of Environment, 2017, 199, pp. 241-255

[7]

Nielsen, A.A.: ‘The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data’, IEEE Transactions on Image processing, 2007, 16, (2), pp. 463-478

Digital Library

[8]

Wei, Y., Liu, H., Song, W., Yu, B., and Xiu, C.: ‘Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with pseudo invariant features’, Landscape and Urban Planning, 2014, 128, pp. 1-13

[9]

Zhao, W., Wang, Z., Gong, M., and Liu, J.: ‘Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network’, IEEE Transactions on Geoscience and Remote Sensing, 2017, 55, (12), pp. 7066-7080

[10]

Zhang, M., and Shi, W.: ‘A feature difference convolutional neural network-based change detection method’, IEEE Transactions on Geoscience and Remote Sensing, 2020, 58, (10), pp. 7232-7246

[11]

Liu, F., Jiao, L., Tang, X., Yang, S., Ma, W., and Hou, B.: ‘Local restricted convolutional neural network for change detection in polarimetric SAR images’, IEEE transactions on neural networks and learning systems, 2018, 30, (3), pp. 818-833

[12]

Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., and Liu, G.: ‘A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images’, ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166, pp. 183-200

[13]

Du, B., Ru, L., Wu, C., and Zhang, L.: ‘Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images’, IEEE Transactions on Geoscience and Remote Sensing, 2019, 57, (12), pp. 9976-9992

[14]

Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., and Wu, A.Y.: ‘An efficient k-means clustering algorithm: Analysis and implementation’, IEEE transactions on pattern analysis and machine intelligence, 2002, 24, (7), pp. 881-892

[15]

Danielsson, P.-E.: ‘Euclidean distance mapping’, Computer Graphics and image processing, 1980, 14, (3), pp. 227-248

Cited By

Wang XZhao KZhao XLi S(2023)TriTF: A Triplet Transformer Framework Based on Parents and Brother Attention for Hyperspectral Image Change DetectionIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.326096961(1-13)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3260969

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ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 2021

699 pages

ISBN:9781450385053

DOI:10.1145/3508546

Copyright © 2021 ACM.

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Published: 25 February 2022

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ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 22 - 24, 2021

Sanya, China

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Cited By

Wang XZhao KZhao XLi S(2023)TriTF: A Triplet Transformer Framework Based on Parents and Brother Attention for Hyperspectral Image Change DetectionIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.326096961(1-13)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3260969

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