I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification

Hyperspectral image data are widely used in real life because it contains rich spectral and spatial information. Hyperspectral image classification is to distinguish different functions based on different features. The computer performs quantitative analysis through the captured image and classifies each pixel in the image. However, the traditional deep learning-based hyperspectral image classification technology, due to insufficient spatial-spectral feature extraction, too many network layers, and complex calculations, leads to large parameters and optimizes hyperspectral images. For this reason, I proposed the I3D-CNN model. The number of classification parameters is large, and the network is complex. This method uses hyperspectral image cubes to directly extract spectral-spatial coupling features, adds depth separable convolution to 3D convolution to reextract spatial features, and extracts the parameter amount and calculation time at the same time. In addition, the model removes the pooling layer to achieve fewer parameters, smaller model scale, and easier training effects. The performance of the I3D-CNN model on the test datasets is better than other deep learning-based methods after comparison. The results show that the model still exhibits strong classification performance, reduces a large number of learning parameters, and reduces complexity. The accuracy rate, average classification accuracy rate, and kappa coefficient are all stable above 95%.

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Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/767/1/012019 ◽

2021 ◽

Vol 767 (1) ◽

pp. 012019

Author(s):

M A A M Abidin ◽

H Z M Shafri ◽

M M A Al-Habshi ◽

N S N Shaharum

Keyword(s):

Deep Learning ◽

Image Classification ◽

Hyperspectral Image ◽

Learning Algorithm ◽

State Of The Art ◽

The State ◽

Hyperspectral Image Classification ◽

Deep Learning Algorithm

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Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

Remote Sensing ◽

10.3390/rs13030335 ◽

2021 ◽

Vol 13 (3) ◽

pp. 335

Author(s):

Yuhao Qing ◽

Wenyi Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Principal Component ◽

Hyperspectral Image Classification ◽

Deep Network ◽

Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

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