Abstract
Classification is the process of setting class labels to pixels based on some obtained properties. Hyperspectral images (HSI) have very high dimensionality, which results in higher cost and complexity for analyzing and classifying them as superfast processors and large storage devices are required. Moreover, due to limited training samples and labeled data, classification remains an arduous task. Many methods have been presented till now for classification of HSI based on traditional methods that use handcrafted features beforehand, principal component analysis and its variations, decision trees, random forests, SVM-based methods, and neural networks, but most of these consider only the spectral information for classification resulting in low classification accuracy. Nowadays, increasing spatial resolution of HSI demands obtaining spatial data for further improving classification performance. We, therefore, present a classification method which obtains spectral as well as spatial features using convolutional neural network (CNN) model and then a logistic regression (LR) classifier that uses the activation function softmax for predicting classification results. Our proposed method is compared with considered techniques and tests on HSIs, namely, Indian pines and Pavia University, which have shown better performance regarding parameters such as overall accuracy (OA), average accuracy (AA), and kappa coefficient (K).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Donoho, D.L.: High-dimensional data analysis: the curses and blessings of dimensionality. AMS Math Challenges Lect 13, 178–183 (2000)
Samaniego, L., Bardossy, A., Schulz, K.: Supervised classification of remotely sensed imagery using a modified, k-NN technique. IEEE Trans. Geo. Rem. Sens. 46, 2112–2125 (2008)
Ediriwickrema, J., Khorram, S.: Hierarchical maximum-likelihood classification for improved accuracies. IEEE Trans. Geo. Rem. Sens. 35, 810–816 (1997)
Li, J., Dias, J.M.B., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geo. Rem. Sens. 48(10), 4085–4098 (2010)
Rodarmel, C., Shan, J.: Principal component analysis for hyperspectral image classification. Surveying Land Inf. Sys. 62(2), 115 (2002)
Robila, S.A., Varshney, P.K.: Feature extraction from hyperspectral data using ICA. In: Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, pp. 199–216 (2004)
Melganiand, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geo. Rem. Sens. 42(8), 1778–1790 (2004)
Ghamisi, P., Mura, M.D., Benediktsson, J.A.: A survey on spectral–spatial classification techniques based on attribute profiles. IEEE Trans. Geo. Rem. Sens. 53, 2335–2353 (2015)
Tuia, D., Volpi, M., Mura, M.D., Rakotomamonjy, A., Flamary, R.: Automatic feature learning for spatio-spectral image classification with sparse SVM. IEEE Trans. Geo. Rem. Sens. 52(10), 6062–6074 (2014)
Jia, S., Zhang, X., Li, Q.: Spectral–spatial hyperspectral image classification using regularized low-rank representation and sparse representation-based graph cuts. IEEE J. Sel. Topics App. Earth Obs. Rem. Sens. 8, 2473–2484 (2015)
Mura, M.D., Villa, A., Benediktsson, J.A., Chanussot, J., Bruzzone, L.: Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Trans. Geo. Rem. Sens. Letters 8, 542–546 (2011)
Shen, L., Jia, S.: Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Trans. Geo. Rem. Sens. 49(12), 5039–5046 (2011)
Qian, Y., Ye, M., Zhou, J.: Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans. Geo. Rem. Sens. 51, 2276–2291 (2013)
Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Topics App. Earth Obs. Rem. Sens. 7(6), 2094–2107 (2014)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Chen, Y., Zhao, X., Jia, X.: Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J. Sel. Topics App. Earth Obs. Rem. Sens. 8(6), 1–12 (2015)
Yue, J., Zhao, W., Mao, S., Liu, H.: Spectral–spatial classification of hyperspectral images using deep convolutional neural networks. Rem. Sens. Lett. 6(6), 468–477 (2015)
Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification using convolutional neural networks. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 4959–4962 (2015)
Liang, H., Li, Q.: Hyperspectral imagery classification using sparse representations of convolutional neural network features. Rem. Sens. 8(2), 99 (2016)
Yue, J., Mao, S., Li, M.: A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Rem. Sens. Lett. 7(9), 875–884 (2016)
Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geo. Rem. Sens. 54(10), 6232–6251 (2016)
Zhang, H., Li, Y., Zhang, Y., Shen, Q.: Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network. Rem. Sens. Lett. 8(5), 438–447 (2017)
Zabalza, J., Ren, J., Yang, M., Zhang, Y., Wang, J., Marshall, S., Han, J.: Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing. ISPRS J. Photogrammetry Rem. Sens. 93, 112–122 (2014)
Setiyoko, A., Dharma I.G.W.S., Haryanto, T.: Recent development of feature extraction and classification hyperspectral images: a systematic literature review. J. Phys.: Conf. Ser. 801(1) (2017)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: 30th International Conference on Machine Learning, p. 28 (2013)
Memisevic, R., Zach, C., Hinton, G., Pollefeys, M.: Gated softmax classification. In: Advances in Neural Information Processing Systems, p. 23 (2010)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kalita, S., Biswas, M. (2019). Improved Convolutional Neural Networks for Hyperspectral Image Classification. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_37
Download citation
DOI: https://doi.org/10.1007/978-981-13-1280-9_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1279-3
Online ISBN: 978-981-13-1280-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)