Deep Multigrained Cascade Forest for Hyperspectral Image Classification

Liu, Xiaobo; Cai, Zhihua; Cai, Yaoming; Xu, Yin

doi:10.1109/tgrs.2019.2918587

Cited by 46 publications

(21 citation statements)

References 43 publications

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“…But the classification result using a small number of training data is not ideal. Liu et al [43] proposed a deep multi-grained cascade forest method called dgcForest. First, the cascade forest is embedded in the multi-grained scanning process to obtain deep representative features with high diversity.…”

Section: Hyperspectral Image Classification Methods Based On 2d-3d Cnnmentioning

confidence: 99%

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Cao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The emergence of convolutional neural network (CNN) has greatly promoted the development of hyperspectral image (HSI) classification technology. However, the acquisition of HSI is difficult. Lack of training samples is the primary cause of low classification performance. Traditional CNN-based methods mainly use 2D CNN for feature extraction, which make interband correlations of HSIs underutilized. 3D CNN extracts the joint-spectral-spatial information representation, but it depends on a more complex model. Also, too deep or too shallow network cannot extract the image features well. To tackle these issues, we propose an HSI classification method based on 2D-3D CNN and multi-branch feature fusion. We first combine 2D CNN and 3D CNN to extract image features. Then, by means of the multi-branch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension. Finally, the fused features are passed into several fully connected layers and a softmax layer to obtain the classification results. In addition, our network model utilizes the state-of-the-art activation function Mish to further improve the classification performance. Our experimental results, conducted on four widely used HSI data sets, indicate that the proposed method achieves better performance than the existing alternatives.

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Section: Hyperspectral Image Classification Methods Based On 2d-3d Cnnmentioning

confidence: 99%

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Cao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The neighborhood size is an important parameter that affects the classification performance. To analyze the influence of neighborhood size on classification accuracy, the neighborhood size is set to be 5, 7,9,11,13,15,17,19,21,23,25,27,29,31,33, and 35, respectively. The classification results are shown in Fig.…”

Section: B Parameter Setting and Analysismentioning

confidence: 99%

“…A cascaded RNN model is designed to fully explore the redundant and complementary information of the high-dimensional spectral signature in [28]. In addition to the traditional deep learning models (SAE, CNN, and RNN), some variants of deep learning are also applied to HSI classification, e.g., deep multigrained cascade forest [29] and capsule network [30].…”

Section: Introductionmentioning

confidence: 99%

Deep Multiview Learning for Hyperspectral Image Classification

Liu

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…These methods do not need dimension reduction preprocessing and have been widely studied. To deal with the second problem, some advanced deep learning structures are introduced into HSI classification, such as residual learning [24], dense network [25], [26], cascade network structure [27], deep random forest [28] and so on. These models greatly improve the classification accuracy of HSIs.…”

Section: Introductionmentioning

confidence: 99%

Semisupervised graph convolutional network for hyperspectral image classification

Liu

Gao

et al. 2020

J. Appl. Rem. Sens.

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Deep learning based methods have seen a massive rise in popularity for hyperspectral image classification over the past few years. However, the success of deep learning is attributed greatly to numerous labeled samples. It is still very challenging to use only a few labeled samples to train deep learning models to reach a high classification accuracy. An active deep-learning framework trained by an end-to-end manner is, therefore, proposed by this paper in order to minimize the hyperspectral image classification costs. First, a deep densely connected convolutional network is considered for hyperspectral image classification. Different from the traditional active learning methods, an additional network is added to the designed deep densely connected convolutional network to predict the loss of input samples. Then, the additional network could be used to suggest unlabeled samples that the deep densely connected convolutional network is more likely to produce a wrong label. Note that the additional network uses the intermediate features of the deep densely connected convolutional network as input. Therefore, the proposed method is an end-to-end framework. Subsequently, a few of the selected samples are labelled manually and added to the training samples. The deep densely connected convolutional network is therefore trained using the new training set. Finally, the steps above are repeated to train the whole framework iteratively. Extensive experiments illustrates that the method proposed could reach a high accuracy in classification after selecting just a few samples.

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Deep Multigrained Cascade Forest for Hyperspectral Image Classification

Cited by 46 publications

References 43 publications

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Deep Multiview Learning for Hyperspectral Image Classification

Semisupervised graph convolutional network for hyperspectral image classification

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