Hyperspectral image classification using wavelet transform-based smooth ordering

To efficiently improve the accuracy of hyperspectral image (HSI) classification, the spatial information is usually fused with spectral information so that the classification performance can be enhanced. In this paper, we propose a new classification method called wavelet transform-based smooth ordering (WTSO). WTSO consists of three main components: wavelet transform for feature extraction, spectral–spatial based similarity measurement, smooth ordering based 1D embedding, and construction of final classifier using interpolation scheme. Specifically, wavelet transform is first imposed to decompose the HSI signal into approximate coefficients (ACs) and details coefficients (DCs). Then, to measure the similar level of pairwise samples, a novel metric is defined on the ACs, where the spatial information serves as the prior knowledge. Next, according to the measurement results, smooth ordering is applied so that the samples are aligned in a 1D space (called 1D embedding). Finally, since the reordering samples are smooth, the labels of test samples can be recovered using the simple 1D interpolation method. In the last step, in order to reduce the bias and improve accuracy, the final classifier is constructed using multiple 1D embeddings. The use of wavelet transform in WTSO can also reduce the high dimensionality of HSI data. By converting the hight-dimensional samples into a 1D ordering sequence, WTSO can reduce the computational cost, and simultaneously perform classification for the test samples. Note that in WTSO, the smooth ordering based 1D embedding and interpolation are executed in an iterative manner. And they will be terminated after finite steps. The proposed method is experimentally demonstrated on two real HSI datasets: IndianPines and University of Pavia, achieving promising results.

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Hyperspectral Image Classification Based on Spectral and Spatial Information Using Multi-Scale ResNet

Applied Sciences ◽

10.3390/app9224890 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4890 ◽

Cited By ~ 2

Author(s):

Zong-Yue Wang ◽

Qi-Ming Xia ◽

Jing-Wen Yan ◽

Shu-Qi Xuan ◽

Jin-He Su ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

Computational Cost ◽

Data Cube ◽

Hyperspectral Image Classification ◽

Fixed Size ◽

Multi Scale

Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. In this paper, to make full use of HSI information, we combined spectral and spatial information into a two-dimension image in a particular order by extracting a data cube and unfolding it. Prior to the step of combining, principle component analysis (PCA) is utilized to decrease the dimensions of HSI so as to reduce computational cost. Moreover, the classification block used during the experiment is a convolutional neural network (CNN). Instead of using traditionally fixed-size kernels in CNN, we leverage a multi-scale kernel in the first convolutional layer so that it can scale to the receptive field. To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. Extensive experiments on the datasets from Pavia University and Salinas demonstrate that the proposed method significantly improves the accuracy in HSI classification.

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Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13163131 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3131

Author(s):

Zhongwei Li ◽

Xue Zhu ◽

Ziqi Xin ◽

Fangming Guo ◽

Xingshuai Cui ◽

...

Keyword(s):

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Classification Performance ◽

Generative Adversarial Networks ◽

Hyperspectral Image Classification ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Spatial Features ◽

Virtual Samples

Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguous, and GANs are prone to the mode collapse, which lead the poor generalization abilities ultimately. Moreover, most of these models only consider the extraction of spectral or spatial features. They fail to combine the two branches interactively and ignore the correlation between them. Consequently, the variational generative adversarial network with crossed spatial and spectral interactions (CSSVGAN) was proposed in this paper, which includes a dual-branch variational Encoder to map spectral and spatial information to different latent spaces, a crossed interactive Generator to improve the quality of generated virtual samples, and a Discriminator stuck with a classifier to enhance the classification performance. Combining these three subnetworks, the proposed CSSVGAN achieves excellent classification by ensuring the diversity and interacting spectral and spatial features in a crossed manner. The superior experimental results on three datasets verify the effectiveness of this method.

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Wavelet-based extended morphological profile and deep autoencoder for hyperspectral image classification

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691318500169 ◽

2018 ◽

Vol 16 (03) ◽

pp. 1850016 ◽

Cited By ~ 2

Author(s):

Huiwu Luo ◽

Yuan Yan Tang ◽

Robert P. Biuk-Aghai ◽

Xu Yang ◽

Lina Yang ◽

...

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

State Of The Art ◽

Classification Performance ◽

Fine Tuning ◽

Spectral Information ◽

Data Sets ◽

Spatial Description ◽

Hyperspectral Image Classification ◽

High Level

In this paper, we propose a novel scheme to learn high-level representative features and conduct classification for hyperspectral image (HSI) data in an automatic fashion. The proposed method is a collaboration of a wavelet-based extended morphological profile (WTEMP) and a deep autoencoder (DAE) (“WTEMP-DAE”), with the aim of exploiting the discriminative capability of DAE when using WTEMP features as the input. Each part of WTEMP-DAE is ingenious and contributes to the final classification performance. Specifically, in WTEMP-DAE, the spatial information is extracted from the WTEMP, which is then joined with the wavelet denoised spectral information to form the spectral-spatial description of HSI data. The obtained features are fed into DAE as the original input, where the good weights and bias of the network are initialized through unsupervised pre-training. Once the pre-training is completed, the reconstruction layers are discarded and a logistic regression (LR) layer is added to the top of the network to perform supervised fine-tuning and classification. Experimental results on two real HSI data sets demonstrate that the proposed strategy improves classification performance in comparison with other state-of-the-art hand-crafted feature extractors and their combinations.

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Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1763502 ◽

2020 ◽

Vol 41 (19) ◽

pp. 7541-7564 ◽

Cited By ~ 3

Author(s):

Sadia Zaman Mishu ◽

Boshir Ahmed ◽

Md. Ali Hossain ◽

Md. Palash Uddin

Keyword(s):

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Subspace Detection

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Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13214472 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4472

Author(s):

Tianyu Zhang ◽

Cuiping Shi ◽

Diling Liao ◽

Liguo Wang

Keyword(s):

Image Classification ◽

Data Augmentation ◽

Hyperspectral Image ◽

Classification Performance ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Feature Maps ◽

Spatial Feature ◽

Global Context

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

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A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-443-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 443-449

Author(s):

P. Zhong ◽

Z. Q. Gong ◽

C. Schönlieb

Keyword(s):

Image Classification ◽

Real World ◽

Hyperspectral Image ◽

Classification Performance ◽

Fine Tuning ◽

Hyperspectral Image Classification ◽

Latent Factors ◽

Deep Model ◽

Training Samples ◽

Limited Training Samples

In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons), which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.

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