Objective

2

Hyperspectral images have been investigated and hyperspectral image classification has been widely used in many fields in recent years. Long-distance remote sensing hyperspectral images have a lot of mixed pixels due to the low spatial resolution. The ground features originally have unique spectral characteristics

but the mixed pixels reduce the separability of spectral characteristics of different ground

which increases the difficulty of hyperspectral image classification. Observing the spectral curves of long-distance remote sensing hyperspectral images indicates that the spectral curves of the single type show the tendency of larger difference within the class

and the difference between classes becomes smaller because it is mixed with different types of ground features. If the selected samples are insufficient

then misclassification between classes may occur. Moreover

the classification map has "salt and pepper noise"

which leads to low classification accuracy. The traditional method uses the spectral-spatial feature joint classification method to increase the constraint of spatial information for improving the classification accuracy. This way can correct some misclassification results

but it does not improve the problem of difficult classification between classes caused by mixed pixels. Thus

a strategy based on grouped rolling guidance filtering is proposed in this study. The linear discriminant analysis (LDA) algorithm is used to generate a discriminative guidance image

and rolling guidance filtering is performed on each band of the hyperspectral image. Spectral curves show the trend of intra-class condensation given that the guidance image contains the information of classification

and the distance between classes increases. At the same time

hyperspectral images have lots of bands

and many adjacent bands may be heavily redundant and fail to provide additional discriminative information. The generalization capability of the classifier is limited when high-dimensional bands are fed back

that is

the curse of dimensionality

under the condition of insufficient labeled pixels. Reducing the number of bands

that is

dimensionality reduction

is an effective strategy to solve these challenges. The embedded band selection is the best comprehensive performance at present because it solves various matrix-based optimization objectives consisting of different loss functions and regularization terms. However

it exhibits sparsity. If multiple features are useful and highly correlated

then least absolute shrinkage and selection operator(LASSO) tends to keep one and drop the rest. This condition affects the stability. We aim to improve its ability to select groups of correlated variables and utilize its group effect to improve classification accuracy. Thus

we use the framework of elastic net logistic regression to enhance band selection

the respective strongly separable bands are selected for each class

and the strongly correlated bands can be retained at the same time.

Method

2

We focus on enhancing spectral separability and band separability of mixed pixels in this study. The framework of grouped rolling guidance filtering and elastic net logistic regression is used to enhance class separability in spectral characteristics and band selection. First

the hyperspectral images are divided into groups according to the spectral direction

and the training data of each group by performing the LDA algorithm are used to obtain the first projection vector. The most discriminative guidance image is generated for each group of hyperspectral images. Then

rolling guidance filtering is conducted on each band of the hyperspectral image. Spectral curves show the increasing trend of intra-class condensation and distance between classes. We use the framework of elastic net logistic regression to enhance band selection. By constructing L1 and L2 norm regularization constraints on logistic regression objective function

embedded band selection is performed to select bands with strong separability for each category

and bands with strong correlation can be retained as the classification basis. We use the neighborhood optimization method to improve the classification results.

Result

2

We compare our algorithms with five state-of-the-art classic algorithms on three public datasets

namely

the India Pines

Salinas

and Kennedy Space Center(KSC) datasets. The quantitative evaluation metrics include overall accuracy (OA)

kappa coefficient

and average accuracy. We also provide several classification maps of each method for comparison on India Pines dataset. The experiment is repeated 10 times under each experimental condition to improve its reliability and accuracy

and the average value of the 10 experimental results is taken as the final result. Experimental results show that our model outperforms all other methods on India Pines

Salinas

and KSC datasets. Moreover

the OA of the proposed algorithm has achieved 96.61%

98.66%

and 99.04% on the Indian Pines

Salinas

and KSC datasets

respectively. These values are 1%—4% higher than those of other optimal algorithms. We conduct a series of comparative experiments on Indian Pines to clearly show the effectiveness of different steps of the proposed algorithm. Comparative experiments with a different number of training samples are also performed. The experiment shows that the OA of the proposed method is always the highest under different datasets. By contrast

other comparison algorithms are unstable over different datasets. Therefore

the proposed algorithm can improve the robustness.

Conclusion

2

Experiments on three hyperspectral image datasets show that the proposed algorithm enhances class separability in spectral characteristics and band selection compared with other algorithms. The classification accuracy is also significantly improved. The proposed algorithm is suitable for different datasets and has good robustness as well.