Hyperspectral image classification using CNN with spectral and spatial features integration

Vaddi, Radhesyam; Manoharan, Prabukumar

doi:10.1016/j.infrared.2020.103296

Cited by 107 publications

(38 citation statements)

References 33 publications

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“…The results showed that this residual network achieved high classification accuracies on images of agricultural, urban, and rural areas [ 25 ]. Though the combination of hyperspectral and airborne LiDAR data has rich and complex spectral, textural, and elevation information, the employment of this information for feature selection and extraction is quite challenging [ 26 ], as not all the measurements are significant and useful, and the original feature space may not be the most effective space for representing the data [ 27 ]. Therefore, we propose in this paper a hierarchical-fusion multiscale dilated residual network to classify ground objects based on fused hyperspectral CASI and airborne LiDAR features.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Chang

Zhang

et al. 2020

Sensors

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Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

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Section: Introductionmentioning

confidence: 99%

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Chang

Zhang

et al. 2020

Sensors

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“…In addition, because of CNN’s powerful classification abilities [ 28 ], it was also used to build classification models.…”

Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Xiao

Bai

Gao

et al. 2020

Sensors

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Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380–1030 nm) and near-infrared range (NIR, 874–1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results; all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali.

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“…The classification of image pixels inside a hyperspectral image into numerous categories is known as HSI classification. [11][12][13] The main aim of image classification is to determine the spectrum for each pixel in a hyperspectral image to identify materials, detect processes or locate objects. The technique of classifying pixels into different classes based on pixel values is known as image classification.…”

Section: Background Of Hsi Classificationmentioning

confidence: 99%

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Reddy

Harikiran

2022

J. Spectral Imaging

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Hyperspectral imaging is used in a wide range of applications. When used in remote sensing, satellites and aircraft are employed to collect the images, which are used in agriculture, environmental monitoring, urban planning and defence. The exact classification of ground features in the images is a significant research issue and is currently receiving greater attention. Moreover, these images have a large spectral dimensionality, which adds computational complexity and affects classification precision. To handle these issues, dimensionality reduction is an essential step that improves the performance of classifiers. In the classification process, several strategies have produced good classification results. Of these, machine learning techniques are the most powerful approaches. As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. Moreover, this paper shows the effectiveness of all these techniques for hyperspectral image classification and dimensionality reduction. Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.

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Hyperspectral image classification using CNN with spectral and spatial features integration

Cited by 107 publications

References 33 publications

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

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