Classification-oriented hyperspectral and PolSAR images synergic processing

Li, Tong; Zhang, Junping; Zhao, Honghong; Shi, Cuiping

doi:10.1109/igarss.2013.6721340

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Ref. [255] fused PolSAR and hyperspectral data, and a features concatenation was produced by concatenating the hyperspectral data's features. Then, decision fusion is used to combine the classification results from multiple classifiers.…”

Section: Image Fusionmentioning

confidence: 99%

Polarimetric Imaging via Deep Learning: A Review

Liu

et al. 2023

Remote Sensing

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Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field.

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Section: Image Fusionmentioning

confidence: 99%

Polarimetric Imaging via Deep Learning: A Review

Liu

et al. 2023

Remote Sensing

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“…The PolSAR data are used to classify the non-vegetation area to man-made objects, water, or bare soil. Li et al [31] applied feature level and decision level fusion using hyperspectral and PolSAR data. They combined the parameters of scattering mechanism of the PolSAR data and the features of the hyperspectral image to create a concatenation of features.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion

Hong

Wang

et al. 2019

Remote Sensing

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In remote sensing, hyperspectral and polarimetric synthetic aperture radar (PolSAR) images are the two most versatile data sources for a wide range of applications such as land use land cover classification. However, the fusion of these two data sources receive less attention than many other, because of their scarce data availability, and relatively challenging fusion task caused by their distinct imaging geometries. Among the existing fusion methods, including manifold learning-based, kernel-based, ensemble-based, and matrix factorization, manifold learning is one of most celebrated techniques for the fusion of heterogeneous data. Therefore, this paper aims to promote the research in hyperspectral and PolSAR data fusion, by providing a comprehensive comparison between existing manifold learning-based fusion algorithms. We conducted experiments on 16 state-of-the-art manifold learning algorithms that embrace two important research questions in manifold learning-based fusion of hyperspectral and PolSAR data: (1) in which domain should the data be aligned—the data domain or the manifold domain; and (2) how to make use of existing labeled data when formulating a graph to represent a manifold—supervised, semi-supervised, or unsupervised. The performance of the algorithms were evaluated via multiple accuracy metrics of land use land cover classification over two data sets. Results show that the algorithms based on manifold alignment generally outperform those based on data alignment (data concatenation). Semi-supervised manifold alignment fusion algorithms performs the best among all. Experiments using multiple classifiers show that they outperform the benchmark data alignment-based algorithms by ca. 3% in terms of the overall classification accuracy.

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“…The process of combining or integrating information from different sensors is termed image fusion (Knödel, Lange, and Voigt 2007). The data fusion preserves the primacy of information and utilizes the interdependent information about the multiple sensors (Li et al 2013). Fusion of the data depends on different levels of processing and results in three different levels of fusion methods: pixel, feature, and decision (Pohl and Van Genderen 1998).…”

Section: Introductionmentioning

confidence: 99%