Abstract
The scattering transform, which applies multiple convolutions using known filters targeting different scales of time or frequency, has a strong similarity to the structure of convolution neural networks (CNNs), without requiring training to learn the convolution filters, and has been used for hyperspectral image classification in recent research. This paper investigates the application of the scattering transform framework to hyperspectral unmixing (STFHU). While state-of-the-art research on unmixing hyperspectral data utilizing scattering transforms is limited, the proposed end-to-end method applies pixel-based scattering transforms and preliminary three-dimensional (3D) scattering transforms to hyperspectral images in the remote sensing scenario to extract feature vectors, which are then trained by employing the regression model based on the k-nearest neighbor (k-NN) to estimate the abundance of maps of endmembers. Experiments compare performances of the proposed algorithm with a series of existing methods in quantitative terms based on both synthetic data and real-world hyperspectral datasets. Results indicate that the proposed approach is more robust to additive noise, which is suppressed by utilizing the rich information in both high-frequency and low-frequency components represented by the scattering transform. Furthermore, the proposed method achieves higher accuracy for unmixing using the same amount of training data with all comparative approaches, while achieving equivalent performance to the best performing CNN method but using much less training data.
Highlights
Hyperspectral image (HSI), covering hundreds of continuous spectral bands, has been widely used in lots of different applications [1,2,3]
As for the noisy images, the root mean square error (RMSE) values remain stable with the decrement of the training ratio, which indicates that the hyperspectral unmixing approach based on the scattering transform can utilize a small proportion (5%) of samples to train, while obtaining approximate results compared with that based on a larger percentage of samples
The results with blue background correspond to the proposed algorithm, while the orange, pink, and green colored backgrounds represent the RMSE values obtained by utilizing convolution neural networks (CNNs), linear spectral unmixing (LSU) and artificial neural networks (ANNs) separately
Summary
Hyperspectral image (HSI), covering hundreds of continuous spectral bands, has been widely used in lots of different applications [1,2,3]. Pixels in remote sensing HSI often consist of mixtures of different classes of land covers (known as endmembers) [4,5] This mixing phenomenon poses great challenges to HSI processing problems, such as segmentation, classification, location estimation, and recognition [6,7]. Many methods, including statistics-based, geometrical-based, and nonnegative matrix factorization, have been used to solve the linear model, which shows good performances in certain situations [13,14,15,16,17,18]. These linear spectral unmixing (LSU) algorithms usually involve endmember extraction and abundance estimation. It is necessary to develop the nonlinear model for unmixing [21,22,23]
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