Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features

By Y. Qian, Minchao Ye, Jun Zhou. IEEE Transactions on Geoscience and Remote Sensing, 2013

372 citations recorded by the source corpus.

Hyperspectral remote sensing imagery contains rich information on spectral and spatial distributions of distinct surface materials. Owing to its numerous and continuous spectral bands, hyperspectral data enable more accurate and reliable material classification than using panchromatic or multispectral imagery. However, high-dimensional spectral features and limited number of available training samples have caused some difficulties in the classification, such as overfitting in learning, noise sensitiveness, overloaded computation, and lack of meaningful physical interpretability. In this paper, we propose a hyperspectral feature extraction and pixel classification method based on structured sparse logistic regression and 3-D discrete wavelet transform (3D-DWT) texture features. The 3D-DWT decomposes a hyperspectral data cube at different scales, frequencies, and orientations, during which the hyperspectral data cube is considered as a whole tensor instead of adapting the data to a vector or matrix. This allows the capture of geometrical and statistical spectral-spatial structures. After the feature extraction step, sparse representation/modeling is applied for data analysis and processing via sparse regularized optimization, which selects a small subset of the original feature variables to model the data for regression and classification purpose. A linear structured sparse logistic regression model is proposed to simultaneously select the discriminant features from the pool of 3D-DWT texture features and learn the coefficients of the linear classifier, in which the prior knowledge about feature structure can be mapped into the various sparsity-inducing norms such as lasso, group, and sparse group lasso. Furthermore, to overcome the limitation of linear models, we extended the linear sparse model to nonlinear classification by partitioning the feature space into subspaces of linearly separable samples. The advantages of our methods are validated on the real hyperspectral remote sensing data sets.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/1301138fd1df0e08c994f9eb6fe41b75fe8b7f06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Shutao Li
2. Jun Yu Li
3. Lin He
4. Chenying Liu
5. Sen Jia
6. Yongqiang Zhao
7. Shiqi Yu
8. Zhaowei Shang

In Media Technology

1. Shutao Li
2. Jun Yu Li
3. Lin He
4. Chenying Liu
5. Sen Jia
6. Yongqiang Zhao
7. Shiqi Yu
8. Zhaowei Shang

Citations in the corpus

• Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, methodology): “ences [20]–[22] also introduced wavelet features; specifically, [22] used a wavelet soft-shrinkage denoising strategy.”
• Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017, neutral): “For example, a series of 3D wavelet filters [23], 3D Gabor filters [24], or 3D scattering wavelet filers [25] generated at different scales and frequencies are applied on hyperspectral data to extract spectral–spatial-combined features.”
• A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, methodology): “Then, a large number of feature extraction methods that integrate the spatial structural and texture information with the spectral features have been developed, including morphological [21, 22, 23], filtering [24, 25], coding [26], etc.”
• Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification (2019, methodology): “In the experiments, we use the default parameters for the compared methods (3-D discrete wavelet transform (3-D-DWT) [17], semisupervised HSI segmetation using multinomial logistic regression (semiMLR) [18], LapSVM [6], S2SL [19], SS-LapSVM [7], and spectral-spatial graph embedding learning (SSGEL) ”
• Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification (2017, methodology): “Other than morphology, spatial structure information of HSI can also be extracted by multiscale analysis, such as 3-D wavelet [9] and 3-D Gabor filtering [10].”