Affiliation:
1. College of Geography and Environment, Shandong Normal University, Jinan 250014, China
2. Jinan Environmental Research Institute, Jinan 250000, China
3. Department of Geography and the Environment, University of North Texas, Denton, TX 76203, USA
Abstract
Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, a three-dimensional convolutional neural network (3D-CNN) provides a new effective way of hyperspectral classification. However, its capability of data mining in complex urban areas, especially in cloud shadow areas has not been validated. Therefore, a 3D-1D-CNN model was proposed for feature extraction in complex urban with hyperspectral images affected by cloud shadows. Firstly, spectral composition parameters, vegetation index, and texture characteristics were extracted from hyperspectral data. Secondly, the parameters were fused and segmented into many S × S × B patches which would be input into a 3D-CNN classifier for feature extraction in complex urban areas. Thirdly, Support Vector Machine (SVM), Random Forest (RF),1D-CNN, 3D-CNN, and 3D-2D-CNN classifiers were also carried out for comparison. Finally, a confusion matrix and Kappa coefficient were calculated for accuracy assessment. The overall accuracy of the proposed 3D-1D-CNN is 96.32%, which is 23.96%, 11.02%, 5.22%, and 0.42%, much higher than that of SVM, RF, 1D-CNN, or 3D-CNN, respectively. The results indicated that 3D-1D-CNN could mine spatial-spectral information from hyperspectral data effectively, especially that of grass and highway in cloud shadow areas with missing spectral information. In the future, 3D-1D-CNN could also be used for the extraction of urban green spaces.
Funder
Natural Science Foundation of Shandong Province
National Youth Science Fund Project of National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference43 articles.
1. Lynch, P., Blesius, L., and Hines, E. (2020). Classification of Urban Area Using Multispectral Indices for Urban Planning. Remote Sens., 12.
2. Gadal, S., Ouerghemmi, W., Gadal, S., and Ouerghemmi, W. (2022, September 05). Morpho-Spectral Recognition of Dense Urban Objects by Hyperspectral Imagery Morpho-Spectral Recognition of Dense Urban Objects by Hyperspectral Imagery. Available online: http://.
3. Qamar, F., and Dobler, G. (2020). Pixel-Wise Classification of High-Resolution Ground-Based Urban Hyperspectral Images with Convolutional Neural Networks. Remote Sens., 12.
4. Multiscale graph cut based classification of urban hyperspectral imagery;Xin;Proceedings of SPIE -The International Society for Optical Engineering,2009
5. Spectral–Spatial Feature Extraction for HSI Classification Based on Supervised Hypergraph and Sample Expanded CNN;Kong;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2018
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