Application of Hyperspectral Image Classification Based on Overlap Pooling
Tóm tắt
Convolutional neural networks (CNN) are increasingly being used in hyperspectral image (HSI) classification. However, most pooling methods are non-overlap pooling and ignore the influence of neighboring pixels on image characteristics, thereby limiting network classification accuracy. This work presents a deep CNN that is based on overlap pooling; in this model, non-overlap pooling is replaced with overlap pooling to improve the accuracy of feature extraction. However, overlap pooling introduces additional noise while improving feature accuracy. We have found that different combinations of max pooling and mean pooling can effectively solve the problem and significantly improve classification performance. The best pooling combination (max–mean–mean) for HSI classification is obtained after verification through experiments. A rectified linear unit activation function layer and the softmax loss classification model are combined to improve overall classification accuracy. Experiments on three HSI data sets, namely, Indian Pines, Salinas and Pavia University, show that the CNN model can increase overall accuracy to 95.66, 97.8 and 97.48%, respectively. Compared with deep network models such as deep belief network and non-overlap CNN, the proposed model has significantly improved the classification accuracy, and thus verifying the high accuracy of feature extraction of overlap pooling in CNN.
Tài liệu tham khảo
Benediktsson JA, Ghamisi P (2015) Spectral–spatial classification of hyperspectral remote sensing images. Artech House, Boston
Du Q, Zhang L, Zhang B, Tong X, Du P, Chanussot J (2013) Foreword to the special issue on hyperspectral remote sensing: theory, methods, and applications. IEEE J Sel Top Appl Earth Observ Remote Sens 6(2):459–465
Younan N, Aksoy S, King R (2012) Foreword to the special issue on pattern recognition in remote sensing. IEEE J Sel Top Appl Earth Observ Remote Sens 5(5):1331–1334
Chang C-I (2013) Hyperspectral data processing: algorithm design and analysis. Wiley, New York
Blanzieri E, Melgani F (2008) Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans Geosci Remote Sens 46(6):1804–1811
Gao L et al (2015) Subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(2):349–353
Ma X, Geng J, Wang H (2015) Hyperspectral image classification via contextual deep learning. EURASIP J Image Video Process 20(1):1–12
Zabalza J et al (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185(12):1–10
Ma X, Wang H, Geng J (2016) Spectral–spatial classification of hyperspectral image based on deep auto-encoder. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1–13
Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107
Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097–1105
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 248–255
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Zhao W, Du S (2016) Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J Photogramm Remote Sens 113:155–165
Zhao W, Du S (2016) Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554
Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362
Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Proceedings of IGARSS, pp 4959–4962
Li W, Wu G, Zhang F et al (2016) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853
Chen Y, Jiang H, Li C et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Qian Y, Ye M, Zhou J (2012) Hyperspectral image classification based structured sparse logistic regression and three-dimensional wavelet texturefeatures. IEEE Trans Geosci Remote Sens 51(4):2276–2291
Shen L, Jia S (2011) Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 49(12):5039–5046
Tang YY, Lu Y, Yuan H (2015) Hyperspectral image classification based on three-dimensional scattering wavelet transform. IEEE Trans Geosci Remote Sens 53(5):2467–2480
Li W et al (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 53(7):3681–3693
Waske B, van der Linden S, Benediktsson JA, Rabe A, Hostert P (2010) Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans Geosci Remote Sens 48(7):2880–2889
Hu W, Huang Y, Wei L, Zhang F, Li H (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens. 2015, Art. no. 258619. https://doi.org/10.1155/2015/258619
Li W, Wu G, Zhang F, Du Q (2017) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853
Santara A et al (2016) BASS net: band-adaptive spectral–spatial feature learning neural network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(9):5293–5301