Cube-CNN-SVM: A Novel Hyperspectral Image Classification Method

Leng, Jinsong; Li, Tao; Bai, Gang; Dong, Qiankun; Han, Dong

doi:10.1109/ictai.2016.0158

Cited by 61 publications

(33 citation statements)

References 27 publications

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“…The hybrid Conv1D-RF network uses the high-dimensional features extracted by Conv1D and leverages the advantages of RF to replace the fully connected (FC) layer to make the final decision. Similar to the hybrid CNN-SVM [33] network, the CNN is used for feature extraction and the SVM classifier is used for classification. The proposed Conv1D-RF network uses one-dimensional convolution (Conv1D), which has great potential in temporal feature representation.…”

Section: One-dimensional Convolution Combined With Random Forest (Conmentioning

confidence: 99%

“…The earliest integrated algorithm to combine the classification advantages of multiple classifiers was the "minority obeying majority" approach, which has certain limitations [32]. Other methods mainly present a hybrid model that leverages the synergy of CNN and SVM, with the CNN classifier used for feature extraction and the SVM classifier used for classification [33]. Furthermore, the weights are assigned to multiple classifiers to make a final decision.…”

Section: Introductionmentioning

confidence: 99%

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Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Yang

et al. 2020

Remote Sensing

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Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

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Section: One-dimensional Convolution Combined With Random Forest (Conmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Yang

et al. 2020

Remote Sensing

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“…However, the above framework only takes the spectral characteristics into account and some spatial information will lost. Thus, cube-CNN [25] and 3D-CNN [28] are proposed to extract spectral-spatial features to improve the classification performance. How to organize the cube is mentioned in the previous sub section, there mainly point out the cube data or 3D data are convolved by 3D kernels as input to feed into net.…”

Section: B Cnn Based Hyperspectral Image Classificationmentioning

confidence: 99%

HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image

Luo

Zou

Yao

et al. 2018

2018 International Conference on Audio, Language and Image Processing (ICALIP)

Self Cite

121

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With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN. Firstly, the spectral-spatial feature is extracted from a target pixel and its neighbors. Then, a number of one-dimensional feature maps, obtained by convolution operation on spectral-spatial features, are stacked into a two-dimensional matrix. Finally, the two-dimensional matrix considered as an image is fed into standard CNN. This is why we call it HSI-CNN. In addition, we also implements two depth network classification models, called HSI-CNN+XGBoost and HSI-CapsNet, in order to compare the performance of our framework. Experiments show that the performance of hyperspectral image classification is improved efficiently with HSI-CNN framework. We evaluate the model's performance using four popular HSI datasets, which are the Kennedy Space Center (KSC), Indian Pines (IP), Pavia University scene (PU) and Salinas scene (SA). As far as we concerned, HSI-CNN has got the state-of-art accuracy among all methods we have known on these datasets of 99.28%, 99.09%, 99.42%, 98.95% separately.

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“…During recent years, the data sources commonly used include synthetic aperture radar [6], multispectral satellite images with medium or high-spatial resolution (e.g., MODIS and Landsat), and hyperspectral images [7][8][9]. Especially for multispectral and hyperspectral remote sensing data, they have the advantages of wide coverage, high resolution, rich spectral information and spatial information, multiple data sources, and low data cost, which provides abundant data support for sea ice detection [10]. However, remote sensing images contain tens to hundreds of bands, and there is a strong correlation between the spectral bands.…”

Section: Introductionmentioning

confidence: 99%

Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification

Han

Wang

Zhou

et al. 2020

Mathematical Problems in Engineering

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Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.

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Cube-CNN-SVM: A Novel Hyperspectral Image Classification Method

Cited by 61 publications

References 27 publications

Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image

Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification

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