A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification

Gong, Zhiqiang; Zhong, Ping; Yu, Yang; Hu, Weidong; Li, Shutao

doi:10.1109/tgrs.2018.2886022

Cited by 205 publications

(115 citation statements)

References 35 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…Compared with DNN, this model saves a lot of time. Gong et al [44] proposed a neural network with multi-scale convolution (MS-CNN). This model takes advantage of both determination-point-process (DPP) based diversity-promoting deep metrics and multi-scale features for effective HSI classification.…”

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

confidence: 99%

“…However, utilizing spectral or spatial information alone is not enough to extract features with sufficient discrimination. In recent years, researchers tended to combine spectral and spatial information [26]- [31] to deal with classification tasks, which achieved good performance. We now provide a brief summary of related work for spectral-spatial-based methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

102

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

Section: Introductionmentioning

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

102

View full text Add to dashboard Cite

show abstract

“…More recently, various convolution neural networks with multiscale spatial-spectral features have been introduced for hyperspectral image classification [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Jiao et al [28] used a pooling operation to generate multiple images from HSI, and a pretrained VGG-16 was introduced to extract multiscale features.…”

Section: Introductionmentioning

confidence: 99%

“…Liang et al [29] also used pretrained VGG-16 to extract multiscale spatial structures and proposed an unsupervised cooperative sparse autoencoder method to fuse deep spatial features and spectral information. Multiscale feature extraction has also been proposed using multiple convolution kernel sizes and determinantal point process priors [30]. An automatic design CNN was introduced with automatic 1-D Auto-CNN and 3-D Auto-CNN [31].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

et al. 2019

View full text Add to dashboard Cite

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.

show abstract

“…Recently, a novel framework called multiple convolutional layers fusion, which aims to fuse extracted information from different convolutional layers for HSI classification, was proposed by Zhao et al, (2019). Moreover, the multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification was developed by Gong et al, (2019). Their CNN consists a multiscale filter bank, a concatenate layer to combine these multiscale features, and a fully connected layer to extract global features.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification by Exploiting Convolutional Neural Networks

Hosseiny

Rastiveis

Daneshtalab

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

High spectral dimensionality of hyperspectral images makes them useful data resources for earth observation in many remote sensing applications. In this case, the convolutional neural network (CNN) can help to extract deep and robust features from hyperspectral images. The main goal of this paper is to use deep learning concept to extract deep features from hyperspectral datasets to achieve better classification results. In this study, after pre-processing step, data is fed to a CNN in order to extract deep features. Extracted features are then imported in a multi-layer perceptron (MLP) network as our selected classifier. Obtained classification accuracies, based on training sample size, vary from 94.3 to 97.17% and 92.35 to 98.14% for Salinas and Pavia datasets, respectively. These results expressed more than 10% improvements compared to the classic MLP classification technique.

show abstract

A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification

Cited by 205 publications

References 35 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

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

Hyperspectral Image Classification by Exploiting Convolutional Neural Networks

Contact Info

Product

Resources

About