Proceedings ArticleDOI
Multi-Scale Dilated Residual Convolutional Neural Network for Hyperspectral Image Classification
Kumari Pooja,Rama Rao Nidamanuri,Deepak Mishra +2 more
- pp 1-5
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TLDR
This paper exploits CNN-based method along with multi-scale and dilated convolution with residual connection concepts for hyperspectral image classification on exclusive real time data set.Abstract:
Recently, deep Convolutional Neural Networks (CNNs) have been extensively studied for hyperspectral image classification. It has undergone significant improvement as compared to conventional classification methods. Yet, there are not much studies have been taken on sub-sampled ground truth dataset in CNN. This paper exploits CNN-based method along with multi-scale and dilated convolution with residual connection concepts for hyperspectral image classification on exclusive real time data set. Two raw and one standard full ground truth Pavia University datasets are used to characterize the performance. Out of raw exclusive datasets, one was taken over urban areas of Ahmedabad, India under ISRO-NASA joint initiative for HYperSpectral Imaging (HYSI) programme, and the other was collected using Hypersec VNIR integrated camera of our institute surroundings from the rooftop of the building.read more
Citations
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Journal ArticleDOI
3-D Channel and Spatial Attention Based Multiscale Spatial–Spectral Residual Network for Hyperspectral Image Classification
TL;DR: The proposed CSMS-SSRN framework can achieve better classification performance on different HSI datasets and enhance the expressiveness of the image features from the two aspects of channel and spatial domains, thereby improving the accuracy of classification.
Journal ArticleDOI
Hierarchical Shrinkage Multiscale Network for Hyperspectral Image Classification With Hierarchical Feature Fusion
TL;DR: Wang et al. as mentioned in this paper proposed a hierarchical shrinkage multiscale feature extraction network by pruning MDMSRB to reduce the redundancy of network structure, and the proposed network hierarchically integrates low-level edge features and high-level semantic features effectively.
Journal ArticleDOI
Dynamic Data Augmentation Method for Hyperspectral Image Classification Based on Siamese Structure
TL;DR: In this article, a dynamic data selection algorithm is proposed to dynamically select the samples that need data augmentation most, which can be nested in Stochastic gradient descent and can be easily implemented.
Journal ArticleDOI
Multiscanning Strategy-Based Recurrent Neural Network for Hyperspectral Image Classification
TL;DR: In this paper , a multiscanning strategy with RNN is proposed to feature the sequential character of the hyperspectral image pixel and fully consider the spatial dependence in the HSI patch.
Proceedings ArticleDOI
A Joint Spatial-Spectral Representation Based Capsule Network for Hyperspectral Image Classification
TL;DR: JSSR-CapsNet as discussed by the authors utilizes an attention module to highlight the validity of sensitive pixels from redundant spatial-spectral features, and builds a dilated convolutional pyramid to take aggregation of pixels with the same class into account, and suppress the interference of noisy pixels to enhance the intra-class consistency.
References
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Journal ArticleDOI
Hyperspectral Image Classification With Deep Learning Models
TL;DR: This paper advocates four new deep learning models, namely, 2-D convolutional neural network, 3-D-CNN, recurrent 2- D CNN, recurrent R-2-D CNN, and recurrent 3- D-CNN for hyperspectral image classification.
Journal ArticleDOI
A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification
TL;DR: Experimental results show that the proposed novel convolutional neural networks with multiscale convolution and diversified metric is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.
Journal ArticleDOI
Learning a Dilated Residual Network for SAR Image Despeckling
TL;DR: Li et al. as mentioned in this paper proposed a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN), which can enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure.
Journal ArticleDOI
Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism
TL;DR: A novel end-to-end 3-D dense convolutional network with spectral-wise attention mechanism (MSDN-SA) for HSI classification that exploits3-D dilated convolutions to simultaneously capture the spectral and spatial features at different scales, and densely connects all 3- D feature maps with each other.
Journal ArticleDOI
Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images
Hou Jiang,Ning Lu +1 more
TL;DR: Quantitative analysis proves that the dehazed results of MRCNN are superior to the traditional methods and other networks, and a comparison to haze-free data illustrates the spectral consistency after haze removal and reveals the changes in the vegetation index.