Learning multiscale and deep representations for classifying remotely sensed imagery

ImageNet classification with deep convolutional neural networks

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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Reducing the dimensionality of data with neural networks

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A fast learning algorithm for deep belief nets

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Principal Component Analysis

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Learning with Kernels

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Principal Component Analysis

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Receptive fields of single neurones in the cat's striate cortex

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Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

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On the mean accuracy of statistical pattern recognizers

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Deep learning-based classification of hyperspectral data

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Learning Hierarchical Features for Scene Labeling

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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

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Distributed asynchronous deterministic and stochastic gradient optimization algorithms

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How does the brain solve visual object recognition?

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Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex.

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Building high-level features using large scale unsupervised learning

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Signal Theory Methods in Multispectral Remote Sensing

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Composite Kernels for Hyperspectral Image Classification

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Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles

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Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles

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Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods

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Classification and feature extraction for remote sensing images from urban areas based on morphological transformations

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Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

Li, J., Bioucas‐Dias, J. M., & Plaza, A. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2162649 · Cited by 732 · see citing works

Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

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An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery

Huang, X., & Zhang, L. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2202912 · Cited by 463 · see citing works

Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

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Proceedings of the 26th Annual International Conference on Machine Learning

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Building Detection and Description from a Single Intensity Image

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Semisupervised Neural Networks for Efficient Hyperspectral Image Classification

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Multiple Feature Learning for Hyperspectral Image Classification

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A spatial–spectral kernel-based approach for the classification of remote-sensing images

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A multiscale texture analysis procedure for improved forest stand classification

Coburn, C. A., & Roberts, A. (2004) · International Journal of Remote Sensing · doi:10.1080/0143116042000192367 · Cited by 274 · see citing works

Exploiting Spectral and Spatial Information in Hyperspectral Urban Data With High Resolution

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Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images

Moser, G., Serpico, S. B., & Benediktsson, J. A. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2211551 · Cited by 243 · see citing works

Adaptive Bayesian contextual classification based on Markov random fields

Jackson, Q., & Landgrebe, D. A. (2002) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2002.805087 · Cited by 211 · see citing works

On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery

Zhao, W., Guo, Z., Yue, J., Zhang, X., & Luo, L. (2015) · International Journal of Remote Sensing · doi:10.1080/2150704x.2015.1062157 · Cited by 194 · see citing works

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

Huang, X., Lu, Q., & Zhang, L. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.01.008 · Cited by 152 · see citing works

Learning Conditional Random Fields for Classification of Hyperspectral Images

Zhong, P., & Wang, R. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2045034 · Cited by 139 · see citing works

Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

Tuia, D., Flamary, R., & Courty, N. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.01.006 · Cited by 102 · see citing works

A multiscale urban complexity index based on 3D wavelet transform for spectral–spatial feature extraction and classification: an evaluation on the 8-channel WorldView-2 imagery

Huang, X., & Zhang, L. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2011.614287 · Cited by 22 · see citing works

Deep learning and process understanding for data-driven Earth system science

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673 · see citing works

Deep learning in remote sensing applications: A meta-analysis and review

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456 · see citing works

Convolutional neural networks for large-scale remote-sensing image classification

Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114 · see citing works

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746 · see citing works

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

Chen, Z., Zhang, Y., Ouyang, C., Zhang, F., & Ma, J. (2018) · Sensors · doi:10.3390/s18030821 · Cited by 111 · see citing works

Benchmarking and scaling of deep learning models for land cover image classification

Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.012 · Cited by 86 · see citing works

Hyperspectral imaging and analysis of mixed algae species in liquid media

Mehrübeoğlu, M., Teng, M. Y., Savage, M., et al. (2012) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2012.6295535 · Cited by 7 · see citing works

A novel reduced-order method for analysis of hyperspectral images

Zhang, Y., & Jiang, L. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/865/1/012027

FT-IR/ATR hyperspectral imaging - applications and data analysis

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A comparative study of hyperspectral unmixing using different algorithm approaches

Yusof, A. Z. M., Manap, R. A., & Darsono, A. M. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v20.i2.pp813-821 · Cited by 2 · see citing works

Current Advances in Hyperspectral Face RecognitionPreprint

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