scholarly journals A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

2020 ◽  
Vol 10 (2) ◽  
pp. 463 ◽  
Author(s):  
Fuding Xie ◽  
Cunkuan Lei ◽  
Cui Jin ◽  
Na An
Keyword(s):  
Classification Scheme ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Principal Component ◽  
Classification Method ◽  
Challenging Problem ◽  
Spatial Classification ◽  
Data Volume ◽  
Simple Linear Iterative Clustering ◽  
Ranking Technique

Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with the appearance of noisy pixels makes it difficult to appropriately measure the similarity between two superpixels. Under the assumption that pixels within a superpixel belong to the same class with a high probability, this paper proposes a novel spectral–spatial HSI classification method at superpixel level (SSC-SL). Firstly, a simple linear iterative clustering (SLIC) algorithm is improved by introducing a new similarity and a ranking technique. The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal component analysis beforehand. In addition, a superpixel-to-superpixel similarity is newly introduced. The defined similarity is independent of the shape of superpixel, and the influence of noisy pixels on the similarity is weakened. Finally, the classification task is accomplished by labeling each unlabeled superpixel according to the nearest labeled superpixel. In the proposed superpixel-level classification scheme, each superpixel is regarded as a sample. This obviously greatly reduces the data volume to be classified. The experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed spectral–spatial classification method over several comparative state-of-the-art classification approaches, in terms of classification accuracy.

scholarly journals An Effective Classification Scheme for Hyperspectral Image Based on Superpixel and Discontinuity Preserving Relaxation

2019 ◽  
Vol 11 (10) ◽  
pp. 1149 ◽  
Author(s):  
Fuding Xie ◽  
Cunkuan Lei ◽  
Jun Yang ◽  
Cui Jin
Keyword(s):  
Spatial Structure ◽  
Classification Scheme ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Novel Technique ◽  
Classification Framework ◽  
Comparison Results ◽  
Simple Linear Iterative Clustering ◽  
Land Covers

Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the complexity of the spatial structure of objects and the spectral similarity between different objects. In this study, an effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation (DPR) is proposed to discriminate land covers of interest. A novel technique for measuring the similarity of a pair of pixels in HSI is suggested to improve the simple linear iterative clustering (SLIC) algorithm. Unlike the existing application of SLIC technique to HSI, the improved SLIC algorithm can be directly used to segment HSI into superpixels without using principal component analysis in advance, and is free of parameters. Furthermore, the proposed three-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Compared with the existing two-step classification framework, the use of DPR technology in preprocessing significantly improves the classification accuracy. The effectiveness of the proposed method is verified on three public real hyperspectral datasets. The comparison results of several competitive methods show the superiority of this scheme.

scholarly journals Hyperspectral Image Spectral–Spatial Classification Method Based on Deep Adaptive Feature Fusion

2021 ◽  
Vol 13 (4) ◽  
pp. 746
Author(s):  
Caihong Mu ◽  
Yijin Liu ◽  
Yi Liu
Keyword(s):  
Short Term Memory ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Principal Component ◽  
Classification Method ◽  
Spectral Features ◽  
Spatial Classification ◽  
Training Samples ◽  
Adaptive Fusion ◽  
Edge Features

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. Many algorithms focus on the deep extraction of a single kind of feature to improve classification. There have been few studies on the deep extraction of two or more kinds of fusion features and the combination of spatial and spectral features for classification. The authors of this paper propose an HSI spectral–spatial classification method based on deep adaptive feature fusion (SSDF). This method first implements the deep adaptive fusion of two hyperspectral features, and then it performs spectral–spatial classification on the fused features. In SSDF, a U-shaped deep network model with the principal component features as the model input and the edge features as the model label is designed to adaptively fuse two kinds of different features. One comprises the edge features of the HSIs extracted by the guided filter, and the other comprises the principal component features obtained by dimensionality reduction of HSIs using principal component analysis. The fused new features are input into a multi-scale and multi-level feature extraction model for further extraction of deep features, which are then combined with the spectral features extracted by the long short-term memory (LSTM) model for classification. The experimental results on three datasets demonstrated that the performance of the proposed SSDF was superior to several state-of-the-art methods. Additionally, SSDF was found to be able to perform best as the number of training samples decreased sharply, and it could also obtain a high classification accuracy for categories with few samples.

Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

2021 ◽  
Vol 16 (1) ◽  
pp. 56-74
Author(s):  
Weiwei Yang ◽  
Haifeng Song
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Support Vector ◽  
Svm Classifier ◽  
Spatial Classification ◽  
Homogeneous Regions

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

scholarly journals Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haifeng Sima ◽  
Pei Liu ◽  
Lanlan Liu ◽  
Aizhong Mi ◽  
Jianfang Wang
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Color Image ◽  
Matching Pursuit ◽  
Spatial Coherence ◽  
Principal Component ◽  
Distance Constraint ◽  
Sparse Representation Classification ◽  
Simple Linear Iterative Clustering ◽  
Pseudo Color

Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit (OMP) algorithm. At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. Experiments conducted on two real hyperspectral datasets show the superiority and effectiveness of the proposed method.

scholarly journals Feature Line Embedding Based on Support Vector Machine for Hyperspectral Image Classification

2021 ◽  
Vol 13 (1) ◽  
pp. 130
Author(s):  
Ying-Nong Chen ◽  
Tipajin Thaipisutikul ◽  
Chin-Chuan Han ◽  
Tzu-Jui Liu ◽  
Kuo-Chin Fan
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Principal Component ◽  
Training Sample ◽  
Support Vector ◽  
Generative Adversarial Network ◽  
Scatter Matrix ◽  
Feature Line

In this paper, a novel feature line embedding (FLE) algorithm based on support vector machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for improving the performance of the generative adversarial network (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown high discriminative capability in many applications. However, owing to the traditional linear-based principal component analysis (PCA) the pre-processing step in the GAN cannot effectively obtain nonlinear information; to overcome this problem, feature line embedding based on support vector machine (SVMFLE) was proposed. The proposed SVMFLE DR scheme is implemented through two stages. In the first scatter matrix calculation stage, FLE within-class scatter matrix, FLE between-scatter matrix, and support vector-based FLE between-class scatter matrix are obtained. Then in the second weight determination stage, the training sample dispersion indices versus the weight of SVM-based FLE between-class matrix are calculated to determine the best weight between-scatter matrices and obtain the final transformation matrix. Since the reduced feature space obtained by the SVMFLE scheme is much more representative and discriminative than that obtained using conventional schemes, the performance of the GAN in HSI classification is higher. The effectiveness of the proposed SVMFLE scheme with GAN or nearest neighbor (NN) classifiers was evaluated by comparing them with state-of-the-art methods and using three benchmark datasets. According to the experimental results, the performance of the proposed SVMFLE scheme with GAN or NN classifiers was higher than that of the state-of-the-art schemes in three performance indices. Accuracies of 96.3%, 89.2%, and 87.0% were obtained for the Salinas, Pavia University, and Indian Pines Site datasets, respectively. Similarly, this scheme with the NN classifier also achieves 89.8%, 86.0%, and 76.2% accuracy rates for these three datasets.

scholarly journals Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

2021 ◽  
Author(s):  
Chang Tang ◽  
Xinwang Liu ◽  
En Zhu ◽  
Lizhe Wang ◽  
Albert Zomaya
Keyword(s):  
Spectral Clustering ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Principal Component ◽  
Band Selection ◽  
Public Datasets ◽  
Multiple Graph ◽  
Homogeneous Regions ◽  
Reflection Characteristics ◽  
Minimum Noise

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

scholarly journals Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

2018 ◽  
Vol 10 (7) ◽  
pp. 1156 ◽  
Author(s):  
Jacopo Acquarelli ◽  
Elena Marchiori ◽  
Lutgarde Buydens ◽  
Thanh Tran ◽  
Twan Laarhoven
Keyword(s):  
Network Architecture ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Hyperspectral Images ◽  
Class Label ◽  
Spatial Classification ◽  
Increased Risk ◽  
New Learning

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification.

scholarly journals Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification

2020 ◽  
Vol 10 (24) ◽  
pp. 8833
Author(s):  
Álvaro Acción ◽  
Francisco Argüello ◽  
Dora B. Heras
Keyword(s):  
Classification Accuracy ◽  
Classification Scheme ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
Classification Model ◽  
Hyperspectral Image Classification ◽  
Augmentation Techniques ◽  
Simple Linear Iterative Clustering ◽  
Classification Tasks ◽  
Dual Window

Deep learning (DL) has been shown to obtain superior results for classification tasks in the field of remote sensing hyperspectral imaging. Superpixel-based techniques can be applied to DL, significantly decreasing training and prediction times, but the results are usually far from satisfactory due to overfitting. Data augmentation techniques alleviate the problem by synthetically generating new samples from an existing dataset in order to improve the generalization capabilities of the classification model. In this paper we propose a novel data augmentation framework in the context of superpixel-based DL called dual-window superpixel (DWS). With DWS, data augmentation is performed over patches centered on the superpixels obtained by the application of simple linear iterative clustering (SLIC) superpixel segmentation. DWS is based on dividing the input patches extracted from the superpixels into two regions and independently applying transformations over them. As a result, four different data augmentation techniques are proposed that can be applied to a superpixel-based CNN classification scheme. An extensive comparison in terms of classification accuracy with other data augmentation techniques from the literature using two datasets is also shown. One of the datasets consists of small hyperspectral small scenes commonly found in the literature. The other consists of large multispectral vegetation scenes of river basins. The experimental results show that the proposed approach increases the overall classification accuracy for the selected datasets. In particular, two of the data augmentation techniques introduced, namely, dual-flip and dual-rotate, obtained the best results.

scholarly journals Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification

2019 ◽  
Vol 11 (3) ◽  
pp. 223 ◽  
Author(s):  
Kaiqiang Zhu ◽  
Yushi Chen ◽  
Pedram Ghamisi ◽  
Xiuping Jia ◽  
Jón Atli Benediktsson
Keyword(s):  
Network Architecture ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Data Sets ◽  
Spatial Classification ◽  
Training Samples ◽  
Vector Machines ◽  
Kernel Support Vector Machines

Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training samples is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.

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