Land use mapping with evidential fusion of features extracted from polarimetric synthetic aperture radar and hyperspectral imagery

This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

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“…The coherence matrix is obtained as [16]:

T = false[a, b, c false] {false[a, b, c false]}^{T} = [\begin{matrix} T_{11} & T_{12} & T_{13} \\ T_{12}^{*} & T_{22} & T_{23} \\ T_{13}^{*} & T_{23}^{*} & T_{33} \end{matrix}]

…”

Section: Pauli Decompositionmentioning

confidence: 99%

Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

Zhang

2011

Sensors

104

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“…(IDFS) as part of the Earth Observation Application Development Program sponsored by Canada Space Agency [1]. In [2], a novel feature selection algorithm is applied to the greedy modular eigenspaces (GME) to explore a multiclass classification technique.…”

Section: Introduction mentioning

confidence: 99%

Classification-oriented hyperspectral and PolSAR images synergic processing

Zhang

Zhao

et al. 2013

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS

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Classification is one of the most important applications in the field of remote sensing. How to improve the accuracy of classification is the critical topic that has long obsessed the researchers. In this paper, a fusion method based on a synergic use of hyperspectral data and Polarimetric SAR (PolSAR) data is presented. This method consists of two main parts, feature-level fusion and decision-level fusion. In feature-level, parallel feature combination strategy is introduced to classification of remote sensing images. Results of feature-level fusion are used as inputs of decision-level fusion based on fuzzy set theory. The final results are compared with processing of single level and single data set, and it shows that the synergic method proposed in this paper has a superior performance in joint classification of hyperspectral and polarimetric SAR data.

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“…Yatabe [12] compared the research applications of SAR at different radar frequencies (C-, S-and L-band) and found that L-band (24 cm wavelength) was the most effective for discriminating forest and shrubland. Jouan [9] reported on fusion of SAR and hyperspectral imagery to map land cover by using the evidential fusion method. Blaschke [13] extracted information from SAR and hyperspectral data by an object-based approach.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Hyperspectral and L-Band SAR Data to Estimate Fractional Vegetation Cover in a Coastal California Scrub Community

Christopher¹

2012

J Geophys Remote Sensing

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were found between fractional cover mapping from remote sensing and the ground-truth data. The estimation accuracy of fractional coverage mapping from remote sensing in terms of root mean square error (RMSE) was 17%, 12%, and 10%, for the herbaceous, coastal scrub, and bare ground covers, respectively. Decomposition results showed that textural information from L-band SAR strongly supported herbaceous and coastal scrub fractional mapping, while indices features from AVIRIS significantly improved mapping of herbaceous cover and bare ground.

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Land use mapping with evidential fusion of features extracted from polarimetric synthetic aperture radar and hyperspectral imagery

Cited by 24 publications

References 31 publications

Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

Classification-oriented hyperspectral and PolSAR images synergic processing

Fusion of Hyperspectral and L-Band SAR Data to Estimate Fractional Vegetation Cover in a Coastal California Scrub Community

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