A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features

Ran, Lingyan; Zhang, Yanning; Wei, Wei; Zhang, Qilin

doi:10.3390/s17102421

Cited by 52 publications

(20 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where f r 12 represents the correctly classified samples by the first classifier and incorrectly classified samples by the second one. According to state of the art, the value of Z which reflects the significant difference of one classifier from another is estimated between 1.96 [40] and 2.58 [41].…”

Section: Evaluation Methodsmentioning

confidence: 99%

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

2019

View full text Add to dashboard Cite

Hyperspectral imaging is known for its rich spatial-spectral information. The spectral bands provide the ability to distinguish substances spectra which is substantial for analyzing materials. However, highdimensional data volume of hyperspectal images is problematic for data storage. In this paper, we present a lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients. The 3D wavelet transform is applied to sparsely represent the hyperspectral images (HSI). A support vector machine regression (SVR) is then applied on wavelet details and provides vector supports and weights which represent wavelet texture features. To achieve the best possible overall rate-distortion performance after regression, entropy encoding based on run-length encoding (RLE) and arithmetic encoding are used. To preserve the spatial pertinent information of the image, the lowest subband wavelet coefficients are furthermore encoded by a lossless coding with differential pulse code modulation (DPCM). Spectral and spatial redundancies are thus substantially reduced. Experimental tests are performed over several hyperspectral images from airborne and spaceborne sensors and compared with the main existing algorithms. The obtained results show that the proposed compression method has high performances in terms of rate-distortion and spectral fidelity. Indeed, high PSNRs and classification accuracies, which could exceed 40.65 dB and 75.8 % respectively, are observed for all decoded HSI images and overpass those given by

show abstract

Section: Evaluation Methodsmentioning

confidence: 99%

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

2019

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm was compared with some recent classification methods. The results of the hyperspectral reference dataset classification using the HCDA algorithm were compared with the results of other algorithms in previous studies [13,[42][43][44][45][46][47][48][49][50][51][52]. The overall accuracy of the reference dataset classification for various algorithms indicates that HCDA results were better than results of other algorithms.…”

Section: Nomentioning

confidence: 99%

Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification

et al. 2019

View full text Add to dashboard Cite

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information.

show abstract

“…However, due to the high diversity of depicted materials, they are highly reliant on domain knowledge to determine which features are important for the classification task. A large number of deep learning models, capable of automatically discovering and learning semantic features, have been developed to tackle HSI classification problems [24,[40][41][42][43]. Chen et al [40] introduced the concept of deep learning into hyperspectral data classification for the first time.…”

Section: Hyperspectral Image Classificationmentioning

confidence: 99%

“…Chen et al [41] employed several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. Ran et al [42] proposed a spatial pixel pair feature that better exploits both the spatial/contextual information and spectral information for HSI classification. In [24], the image was firstly segmented into different homogeneous parts, called superpixels.…”

Section: Hyperspectral Image Classificationmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features

Cited by 52 publications

References 51 publications

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification

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

Contact Info

Product

Resources

About