Classification of Hyperspectral Remote Sensing Image Using Hierarchical Local-Receptive-Field-Based Extreme Learning Machine

Lv, Qi; Niu, Xiamu; Dou, Yong; Xu, Jiaqing; Lei, Yuanwu

doi:10.1109/lgrs.2016.2517178

Cited by 26 publications

(21 citation statements)

References 19 publications

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“…Extensive experiments indicate the effectiveness of the proposed method, which achieves comparable or better accuracy performance than existing methods, such as deep neural networks [28], multiple kernel learning [29], probabilistic class structure regularized sparse representation graph [30,31] and low-rank Gabor filtering [12] (see the results in Table 10).…”

Section: Introductionmentioning

confidence: 90%

“…Moreover, some CNN-based methods use preprocessing, often PCA, to either build a low dimensional set of non-linear input features or to extract additional information (e.g., edge detection). These methods include [20], a deep CNN with 2D input patches and R-PCA [33], a deep stacked auto-encoder with 2D input patches and PCA [18], a contextual deep CNN [36], a multi-hypothesis prediction [12], a low-rank Gabor filtering method [19], a deep CNN with 1D pixel spectra [23], a deep CNN with 1D pixel spectra, 2D pixel patches or 3D pixel cubes [21], a deep CNN with 1D pixel spectra and [28] a deep CNN with uniform smoothing kernel and 1D pixel spectra. Fortunately, the authors of the latter method shared the source code with us, which we could then use in our comparative experimental analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Recent state-of-the-art methods exploit this property in different ways, such as the use of patches to train a (deep) neural network [20,33,18], the generation of discriminatory features using Gabor filters [36,12] or the use of additive Gaussian noise in addition to linear combinations of training pixels [28].…”

Section: Trick 2: Smoothing-based Data Augmentationmentioning

confidence: 99%

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Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

et al. 2018

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Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectrallocality-aware regularization term and smoothing-and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification.

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Section: Introductionmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Trick 2: Smoothing-based Data Augmentationmentioning

confidence: 99%

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Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

et al. 2018

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“…Extreme Learning Machine( ELM) was originally proposed for generalized single-hidden layer feedforward neural networks [25][26][27]. Due to its fast learning speed, good generalization ability, and ease of implementation, ELM has become a popular research topic and been widely used for supervised learning of HSI [28][29][30][31][32]. Although ELM is successfully used for supervised learning in hyperspectral image classification, it is also difficult to avoid a large number of labeled samples.…”

Section: Introductionmentioning

confidence: 99%

Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Fang

Cai

et al. 2020

Sensors

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Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

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“…To overcome this difficulty, ELM-LRF was proposed recently by combining the network structure of CNN with the learning strategy of ELM. The efficiency of ELM-LRF has been proved empirically in a few applications and generalizations [20], [21], [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Extreme learning machine with local connections

Yang

Yao

et al. 2019

Neurocomputing

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This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen rather than to be learned. To this end, we propose a modified ELM, called ELM-LC (ELM with local connections), which is designed for the sparsification of the input-hidden weights as follows: The hidden nodes and the input nodes are divided respectively into several corresponding groups, and an input node group is fully connected with its corresponding hidden node group, but is not connected with any other hidden node group. As in the usual ELM, the hiddeninput weights are randomly given, and the hidden-output weights are obtained through a least square learning. In the numerical simulations on some benchmark problems, the new ELM-CL behaves better than the traditional ELM.Index Terms-Extreme learning machine (ELM), local connections, sparsification of input-hidden weights, high dimensional input data.

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Classification of Hyperspectral Remote Sensing Image Using Hierarchical Local-Receptive-Field-Based Extreme Learning Machine

Cited by 26 publications

References 19 publications

Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Extreme learning machine with local connections

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