Semantic Feature Extraction Method for Hyperspectral Crop Classification

Girish Baabu M C, Padma M C

Abstract


Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance. 

HyperspectHyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance.


Keywords


Crop classification, Deep learning, Dimension reduction, Feature extraction, Feature selection, Hyperspectral image, Machine learning.

References


Gerland, P.; Raftery, A.E.; Ševˇcíková, H.; Li, N.; Gu, D.; Spoorenberg, T.; Alkema, L.; Fosdick, B.K.; Chunn, J.; Lalic, N.; et al. World population stabilization unlikely this century. Science 2014, 346, 234–237.

Whitcraft, A.K.; Becker-Reshef, I.; Justice, C.O. A framework for defining spatially explicit earth observation requirements for a global agricultural monitoring initiative (GEOGLAM). Remote Sens. 2015, 7, 1461–1481.

Fuhrer, J.; Gregory, P.J. Climate Change Impact and Adaptation in Agricultural Systems; CABI: Wallingford, CT, USA, 2014; ISBN 9781780642895.

Mahalanobis, A., Vijaya Kumar, B. V. K., & Sims, S. R. F. Distance-classifier correlation filters for multiclass target recognition. Applied Optics, 35(17), 3127-3133, 2013.

F. L¨ow, U. Michel, S. Dech, and C. Conrad, “Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines,” ISPRS J. Photogrammetry Remote Sens., vol. 85, pp. 102–119, 2013.

S. Murmu and S. Biswas, “Application of fuzzy logic and neural network in crop classification: A review,” Aquatic Procedia, vol. 4, pp. 1203–1210, 2015.

K. Tatsumi, Y. Yamashiki, M. A. C. Torres, and C. L. R. Taipe, “Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data,” Comput. Electron. Agriculture, vol. 115, pp. 171–179, 2015.

M. Gong, M. Zhang, and Y. Yuan, “Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 544–557, Jan 2016.

W. Sun and Q. Du, “Graph-regularized fast and robust principal component analysis for hyperspectral band selection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 6, pp. 3185–3195, 2018.

H. Zhai, H. Zhang, L. Zhang, and P. Li, “Laplacian-regularized lowrank subspace clustering for hyperspectral image band selection,” IEEE Transactions on Geoscience and Remote Sensing, pp. 1–18, 2018.

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1778–1790, Aug 2004.

M. Zhang, M. Gong, and Y. Chan, “Hyperspectral band selection based on multi-objective optimization with high information and low redundancy,” Applied Soft Computing, vol. 70, pp. 604 – 621, 2018.

P. Hu, X. Liu, Y. Cai, and Z. Cai, “Band selection of hyperspectral images using multiobjective optimization-based sparse self-representation,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, 2018.

Q. Wang, F. Zhang, and X. Li, “Optimal clustering framework for hyperspectral band selection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 10, pp. 5910–5922, Oct 2018.

X. Jiang, X. Song, Y. Zhang, J. Jiang, J. Gao, and Z. Cai, “Laplacian regularized spatial-aware collaborative graph for discriminant analysis of hyperspectral imagery,” Remote Sensing, vol. 11, no. 1, p. 29, 2019.

W. Liao, A. Pizurica, P. Scheunders, W. Philips, and Y. Pi, “Semisupervised local discriminant analysis for feature extraction in hyperspectral images,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 184–198, Jan. 2013.

A. Villa, J. A. Benediktsson, J. Chanussot, and C. Jutten, “Hyperspectral image classification with independent component discriminant analysis,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 12, pp. 4865–4876, 2011.

S. Prasad and L. Mann Bruce, “Limitations of principal components analysis for hyperspectral target recognition,” IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 625–629, 2008.

L. Wang, J. Zhang, P. Liu, K.-K. R. Choo, and F. Huang, “Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification,” Soft Computing, vol. 1, no. 21, pp. 213–221, 2016.

J. Jiang, C. Chen, Y. Yu, X. Jiang, and J. Ma, “Spatial-aware collaborative representation for hyperspectral remote sensing image classification,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 3, pp. 404–408, 2017.

Liang, Yi & Zhao, Xin & Guo, Alan & Zhu, Fei. (2019). Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field, 2019.

A. Santara et al., "BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 9, pp. 5293-5301, Sept. 2017.

X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu and J. Paisley, "Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network," in IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2354-2367, May 2018.

Luo, Haowen. Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification, 2018.

Y. Cai, X. Liu and Z. Cai, "BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image," in IEEE Transactions on Geoscience and Remote Sensing. doi: 10.1109/TGRS.2019.2951433.

L. Wang, Y. Feng, Y. Gao, Z. Wang and M. He, "Compressed Sensing Reconstruction of Hyperspectral Images Based on Spectral Unmixing," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 4, pp. 1266-1284, April 2018.

J. Yang and J. Qian, "Hyperspectral Image Classification via Multiscale Joint Collaborative Representation With Locally Adaptive Dictionary," in IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 1, pp. 112-116, Jan. 2018.

X. Kang, S. Li and J. A. Benediktsson, "Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 6, pp. 3742-3752, June 2014.

Li, F, Kazim Ergun, Osman Cihan Kilinc and Y. Qiao. “Vegetation Classification Using Hyperspectral Images.” 2018.

M.C. Girish Babu, M.C. Padma, “A Efficient Solution for Classification of Crops using Hyper Spectral Satellite Images,” International Journal of Innovative Technology and Exploring Engineering (IJITEE)’, ISSN: 2278–3075 (Online), Volume-9 Issue-2, December 2019, Page No. 5204-5211, 2019.




DOI: http://doi.org/10.11591/ijeecs.v22.i3.pp%25p

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