HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network

Rasti, Behnood; Koirala, Bikram; Scheunders, Paul

doi:10.1109/tgrs.2022.3202490

Cited by 14 publications

(4 citation statements)

References 46 publications

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“…In the first step, the encoder was used to linearly estimate the endmember and its corresponding abundance fraction, and in the second step, the bilinear model was used to improve the estimation results of the first part. A blind nonlinear unmixing technique based on the Hapke model and convolution neural network (HapkeCNN) was proposed in [37]. It was trained by a loss function including a quadratic term based on the Hapke model, the reconstruction error of the reflectances, and a minimum volume total variation (TV) term.…”

Section: Model-guided DL Methodsmentioning

confidence: 99%

IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing

Shao

Liu

Xiao

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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At present, an emerging technique called the algorithm unrolling approach has attracted wide attention, because it is capable of developing efficient and interpretable layers to eliminate the black-box nature of deep learning (DL). In this paper, inspired by the sparse unmixing model, we propose a model-driven DL approach, namely, an implicit variable iterative unrolling network (IVIU-Net). First of all, the unmixing performance and adaptive ability of the model are enhanced by introducing learnable parameters into the sparse unmixing algorithm. Then, a specific spatial convolution module is integrated into the network to promote the smoothness of the latent abundance map. Finally, a comprehensive loss function with three terms such as average spectral angle distance, HSIs reconstruction error, and spectral information divergence, is presented to train the IVIU-Net in an unsupervised way. Compared to the unmixing results of most existing data-driven DL algorithms, our network has significant advantages in two folds: 1) it is able to achieve better stability instead of relying heavily on the endmember initialization results; 2) it has better interpretability and robustness in the unmixing procedure. Experimental results on synthetic and real data show that the proposed network outperforms the state-of-the-art in terms of better convergence, faster unmixing speed as well as better accuracy.

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Section: Model-guided DL Methodsmentioning

confidence: 99%

IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing

Shao

Liu

Xiao

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Two real hyperspectral remote sensing images were further applied to evaluate the performance of the proposed FTUPSO in practical scenarios. Due to the absence of ground truth, two quantitative metrics (i.e., RE and SRE) defined in (23) and (24) and qualitative experimental results including endmember curves and abundance maps were adopted to compare the overall unmixing accuracy of the methods. In addition, the execution time for each method was provided.…”

Section: Real Hyperspectral Data Experimentsmentioning

confidence: 99%

“…In [22] the nonlinear mixing effects were modeled by two fully connected hidden layers with a 3D convolutional neural network (CNN) capturing the spatial-spectral information of HSI. To enhance physical interpretability, a fully convolutional deep AE network was combined with the Hapke model in [23], yielding better unmixing results for intimate mixtures.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation

Yin,

Yang

2023

Remote Sensing

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In hyperspectral unmixing, dealing with nonlinear mixing effects and spectral variability (SV) is a significant challenge. Traditional linear unmixing can be seriously deteriorated by the coupled residuals of nonlinearity and SV in remote sensing scenarios. For the simplification of calculation, current unmixing studies usually separate the consideration of nonlinearity and SV. As a result, errors individually caused by the nonlinearity or SV still persist, potentially leading to overfitting and the decreased accuracy of estimated endmembers and abundances. In this paper, a novel unsupervised nonlinear unmixing method accounting for SV is proposed. First, an improved Fisher transformation scheme is constructed by combining an abundance-driven dynamic classification strategy with superpixel segmentation. It can enlarge the differences between different types of pixels and reduce the differences between pixels corresponding to the same class, thereby reducing the influence of SV. Besides, spectral similarity can be well maintained in local homogeneous regions. Second, the polynomial postnonlinear model is employed to represent observed pixels and explain nonlinear components. Regularized by a Fisher transformation operator and abundances’ spatial smoothness, data reconstruction errors in the original spectral space and the transformed space are weighed to derive the unmixing problem. Finally, this problem is solved by a dimensional division-based particle swarm optimization algorithm to produce accurate unmixing results. Extensive experiments on synthetic and real hyperspectral remote sensing data demonstrate the superiority of the proposed method in comparison with state-of-the-art approaches.

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“…Deep autoencoder architectures have been developed that utilize the PPNM to reconstruct the input hyperspectral image [27], [28]. In [29], it was demonstrated that the endmembers of nonlinear datasets can be accurately estimated through the use of deep autoencoder architectures.…”

Section: Introductionmentioning

confidence: 99%

A Supervised Approach for Estimating Fractional Abundances of Binary Intimate Mixtures

Koirala,

Rasti,

Scheunders

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this work, we propose a supervised framework for spectral unmixing of binary intimate mixtures. The core idea is based on geodesic distance measurements and regression to estimate the fractional abundances. The main assumption is that spectral reflectances of binary mixtures form a curve between the two endmembers, and the mixture's relative position on this curve serves as an indicator of its fractional abundances. We propose four novel approaches to approximate this relative position. From this, the fractional abundances are obtained using Gaussian process regression. The proposed framework simultaneously copes with the spectral variability by hypersphere and highdimensional simplex projections. The approach is extensively validated on real datasets, including binary mineral mixtures and industrial clay powder mixtures produced in a laboratory setting, comprising 60 binary mixtures derived from five types of clay powders: Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide, measured by a variety of hyperspectral sensors in the VNIR-SWIR and mid-and longwave infrared regions. A comparison with the linear mixing model and several nonlinear mixing models demonstrates the superiority of the proposed approach.

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HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network

Cited by 14 publications

References 46 publications

IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing

IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing

Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation

A Supervised Approach for Estimating Fractional Abundances of Binary Intimate Mixtures

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