Proceedings Article10.1145/3007669.3007742
A multi-label Hyperspectral image classification method with deep learning features
Cong Wang,Peng Zhang,Yanning Zhang,Lei Zhang,Wei Wei +4 more
- 19 Aug 2016
- pp 127-131
17
TL;DR: A multi-label hyperspectral image classification approach based on deep learning that can well represent the nonlinearity of the mixed pixels in a high dimensional feature space is proposed.
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Abstract: Hyperspectral image (HSI) classification is an important application of HSI analysis, which aims at assigning a class label to each pixel. However, considering that mixed pixels commonly exist in HSI, assigning a unique label to each pixel is imprecise. To better analysis the scene imaged in an HSI, we propose a multi-label hyperspectral image classification approach based on deep learning in this study. First, stacked denoising autoencoder (SDAE) method is used to extract deep features for each pixel without supervision, which can well represent the nonlinearity of the mixed pixels in a high dimensional feature space. Then, multi-label logistic regression method assigns each pixel multi labels. Experimental results on the synthetic data, real hyperspectral data and down-sampling hyperspectral data demonstrate the effectiveness of the proposed method.
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Citations
Deep learning classifiers for hyperspectral imaging: A review
TL;DR: A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques.
903
A survey: Deep learning for hyperspectral image classification with few labeled samples
TL;DR: Although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model.
Two-Stream Deep Architecture for Hyperspectral Image Classification
TL;DR: A novel two-stream deep architecture for HSI classification that combines the stacked denoising autoencoder and deep convolutional neural networks to achieve competitive performance compared with the state-of-the-art methods.
155
When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature
TL;DR: A novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure, improving the classification accuracy with limited labelled samples.
Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer
TL;DR: Wang et al. as discussed by the authors proposed a tomato BW detection model based on some optimal spectral features, including vegetation indexes and principal components (PCs), extracted by the sequential forward selection (SFS), the simulated annealing (SA), and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes.
References
•Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
- 01 Jan 2014
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent,Hugo Larochelle,Yoshua Bengio,Pierre-Antoine Manzagol +3 more
- 05 Jul 2008
TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
P. Vincent
- 01 Jan 2010
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
6.1K
•Journal Article
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TL;DR: Denoising autoencoders as mentioned in this paper are trained locally to denoise corrupted versions of their inputs, which is a straightforward variation on the stacking of ordinary autoencoder.
Classification of hyperspectral remote sensing images with support vector machines
Farid Melgani,Lorenzo Bruzzone +1 more
TL;DR: This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.