Spatial‐spectral feature extraction of hyperspectral images using tensor‐based collaborative graph analysis

Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.

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Automated spectral feature extraction from hyperspectral images to differentiate weedy rice and barnyard grass from a rice crop

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2019.02.018 ◽

2019 ◽

Vol 159 ◽

pp. 42-49 ◽

Cited By ~ 2

Author(s):

Yanchao Zhang ◽

Junfeng Gao ◽

Haiyan Cen ◽

Yongliang Lu ◽

Xiaoyue Yu ◽

...

Keyword(s):

Feature Extraction ◽

Spectral Feature ◽

Hyperspectral Images ◽

Rice Crop ◽

Weedy Rice ◽

Barnyard Grass

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An Efficient Spectral Feature Extraction Framework for Hyperspectral Images

Remote Sensing ◽

10.3390/rs12233967 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3967

Author(s):

Zhen Li ◽

Baojun Zhao ◽

Wenzheng Wang

Keyword(s):

Feature Extraction ◽

Spectral Characteristics ◽

Spectral Feature ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Spectral Information ◽

Testing Time ◽

Discriminative Ability ◽

Feature Extracting ◽

Learning Machine

Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accuracy. In this paper, we design an effective feature extracting framework for the spectra of hyperspectral data. We construct a structured dictionary to encode spectral information and apply learning machine to map coding coefficients. To reduce training and testing time, the sparsity constraint is replaced by a block-diagonal constraint to accelerate the iteration, and an efficient extreme learning machine is employed to fit the spectral characteristics. To optimize the discriminative ability of our model, we first add spectral convolution to extract abundant spectral information. Then, we design shared constraints for subdictionaries so that the common features of subdictionaries can be expressed more effectively, and the discriminative and reconstructive ability of dictionary will be improved. The experimental results on diverse databases show that the proposed feature extraction framework can not only greatly reduce the training and testing time, but also lead to very competitive accuracy performance compared with deep learning models.

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Spatial-Spectral Feature Extraction of Hyperspectral Images using Attribute Profile With Partial Reconstruction and 3-D Gabor Filter Bank

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) ◽

10.1109/icspis48872.2019.9066038 ◽

2019 ◽

Author(s):

Mohammad Dowlatshah ◽

Hassan Ghassemian ◽

Maryam Imani

Keyword(s):

Feature Extraction ◽

Filter Bank ◽

Gabor Filter ◽

Spectral Feature ◽

Hyperspectral Images ◽

Partial Reconstruction ◽

Gabor Filter Bank

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Hyperspectral Image Classification Using Unsupervised Learning Algorithms

10.20944/preprints202106.0706.v1 ◽

2021 ◽

Author(s):

Wafa Fatima ◽

Iqra Ejaz

Keyword(s):

Feature Extraction ◽

Hyperspectral Image ◽

Spectral Feature ◽

Unsupervised Classification ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Classification Algorithms ◽

Classification Methods ◽

Hyperspectral Image Classification ◽

Different Types

Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.

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Spectral–Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression

Remote Sensing ◽

10.3390/rs13010009 ◽

2020 ◽

Vol 13 (1) ◽

pp. 9

Author(s):

Fanqiang Kong ◽

Kedi Hu ◽

Yunsong Li ◽

Dan Li ◽

Shunmin Zhao

Keyword(s):

Feature Extraction ◽

Image Compression ◽

Multispectral Imaging ◽

Rapid Development ◽

Spectral Feature ◽

Multispectral Image ◽

Landsat 8 ◽

Spatial Feature ◽

Spatial Features ◽

Multispectral Image Compression

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

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