Improved GSA Method Based on Deep Convolutional Features

Shuai Wu

doi:10.54097/ajst.v8i1.13990

Authors

Shuai Wu

DOI:

https://doi.org/10.54097/ajst.v8i1.13990

Keywords:

Multi-source remote sensing imagery; GSA; U-net.

Abstract

With the rapid development of remote sensing platforms, sensors and other technologies, remote sensing, as an important technical means of collecting information on land cover and its changes, plays an important role in land cover classification and dynamic monitoring. Due to the limitation of the imaging mechanism of in-orbit optical remote sensing sensors, it is difficult for to take into account both spatial resolution and spectral resolution. There is a need for complementary use of different sensor images and to improve the accuracy of feature interpretation. In this study, based on the Gram-Schmidt Adaptive (GSA) and its classical extension, a neural network-based multi-source remote sensing image fusion method is proposed by combining the U-Net coding and decoding structure with the Non-Local spatial attention mechanism. Using two sets of experimental datasets, three kinds of fusion evaluation metrics without reference and three kinds of fusion evaluation metrics with reference, the improved method and the existing fusion method are compared.

Downloads

Download data is not yet available.

References

ZHANG Bing, GAO Lianru, LI Jiaxin, et al. Progress and prospect of super-resolution fusion research on high/multispectral remote sensing images[J]. Journal of Surveying and Mapping, 2023, 52(7):1074-1089.

Zhang Y , Ji Q . Active and dynamic information fusion for facial expression understanding from image sequences[J]. IEEE Trans Pattern Anal Mach Intell, 2005, 27(5):699-714.

TONG Qingxi, ZHANG Bing, ZHANG Lifu. Frontier progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5): 689-707.

Nencini F , Garzelli A , Baronti S , et al. Remote sensing image fusion using the curvelet transform[J]. Information Fusion, 2007, 8(2):143-156.

Aiazzi B , Alparone L , Baronti S ,et al. 25 years of pansharpening: a critical review and new developments[M]. 2012.

Vivone G , Alparone L , Chanussot J , et al. A Critical Comparison Among Pansharpening Algorithms[J]. IEEE Transactions on Geoence & Remote Sensing, 2015, 53 (5): 2565-2586.

Zhang Liangpei, Shen Huanfeng. Progress and foresight of remote sensing data fusion[J]. Journal of Remote Sensing, 2016, 20(5):12.

LI Shutao,LI Congyu,KANG Xudong. Current status and future outlook of the development of multi-source remote sensing image fusion[J]. Journal of Remote Sensing, 2021, 25(1): 148-166.

Vivone G , Restaino R , Licciardi G A , et al. MultiResolution Analysis and Component Substitution techniques for hyperspectral Pansharpening[C]// Geoscience & Remote Sensing Symposium. ieee, 2014.

Ghassemian, Hassan. a review of remote sensing image fusion methods[J]. Information Fusion, 2016:75-89.

Yokoya N , Grohnfeldt C , Chanussot J . Hyperspectral and Multispectral Data Fusion: a comparative review of the recent literature[J]. IEEE Geoscience & Remote Sensing Magazine, 2017, 5(2):29-56.

Yan Junhua, Zhang Kun, Shi Tianjun, et al. Detection of weak targets on the ground of remote sensing images by fusing multilevel features[J]. Journal of Instrumentation, 2022 (003): 043.

XU Shengjun, ZHANG Ruoxuan, MENG Yuebo,et al. Fusion of fractal geometric features for building segmentation in Resnet remote sensing images [J]. Optical Precision Engineering, 2022, 30(16):15.

Ghamisi P , Rasti B , Yokoya N , et al. Multisource and Multitemporal Data Fusion in Remote Sensing: a Comprehensive Review of the State of the Art[J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(1):6-39.

Vivone G, Alparone L, Garzelli L, and Lolli S. Fast reproducible pansharpening based on instrument and acquisition modelling: AWLP revisited[J]. Remote Sensing, 2019, 11(19):2315:1-2315:23.

Shi Y, Tian Y, Wang Y, et al. Sequential Deep Trajectory Descriptor for Action Recognition With Three-Stream CNN[J]. IEEE Transactions on Multimedia, 2017, 19(7): 1510-1520.

Tian C, Zhuge R, Wu Z, et al. Lightweight image super-resolution with enhanced CNN[J]. Knowledge-Based Systems, 2020, 205: 106235.

Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation[C]//Navab N, Hornegger J, Wells W M, et al. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. cham: Springer International Publishing, 2015: 234-241.

Wang X, Girshick R, Gupta A, et al. Non-local neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. 7794-7803.