# Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

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## Abstract

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## 1. Introduction

- We proposed a modified tSNE through the PCA algorithm to solve the visualization, dimensionality, and computational complexity problem. PCA is used to discover the irrelevant feature bands, aiming to increase the performance of tSNE. The tSNE preserves the intra- and inter-band relationship of the HSI, which is the most effective feature of the HSI for classification and visualization.
- We designed a new blended CNN architecture for feature extraction and classification, which is a sequential combination of 3D- and 2D-CNN to incorporate the spatial and spectral information of HSI, where the combined spectral information contains the wavelengths of the bands and the spatial information contains the location information of the band.
- Two benchmark HSI datasets were used to evaluate the proposed system, namely Indian Pines and SalinusA. Finally, the state-of-the-art comparison table proves the superiority of the proposed model over the mentioned systems.

## 2. Related Work

## 3. Dataset

#### 3.1. Indian Pines

#### 3.2. Salinas

## 4. Proposed Methodology

Algorithm 1 Proposed Method Pseudocode |

Input: Set of Input Dataset ${P}_{i}\in P\left(n\right)$ with dimension $X\times Y\times D$ |

Number of Samples: N, 70% for Training and 30% for Test |

Output: Set of predicted class ${s}_{i}$ |

$\mathbf{Dimension}\mathbf{Reduction}\leftarrow PCA(X\times Y\times D)$ |

$\mathbf{Inter}\mathbf{and}\mathbf{Intra}\mathbf{Class}\mathbf{Feature}\leftarrow tSNE(X\times Y\times d)$ |

define BlendedCNNModel(input=$X\times Y\times B$, outputs=ClassificationLayer): |

$SpectralSpatialFeature\leftarrow Spectral-Spatial\left(input\right)$ |

$SpatialFeature\leftarrow Spatial-Feature\left(SpectralSpatialFeature\right)$ |

$PredictedClass\leftarrow Classifier\left(SpatialFeature\right)$ |

return PredictedClass |

while $i\ne NumEpochs$ do |

$\hspace{1em}$// For Training |

while $Batch\ne NumberBatchTraining$ do |

$\hspace{1em}\hspace{1em}PredictedClass\leftarrow Model\left(Batch\right)$ |

$\hspace{1em}\hspace{1em}Loss\leftarrow Criterion(PredictedClass,Trai{n}_{C}lass)$ |

$\hspace{1em}\hspace{1em}Updatetheloss\leftarrow Loss.backward\left(\right),Optimizer.Step\left(\right)$ |

// For Testing |

while $Batch\ne NumberBatchTesting$ do |

$\hspace{1em}\hspace{1em}PredictedClass\leftarrow Model\left(Batch\right)$ |

$\hspace{1em}\hspace{1em}Output\leftarrow CPerformanceMatrix(PredictedClass,TestClass)$ |

#### 4.1. Principle Component Analysis (PCA) for HSI

#### 4.2. t-Distributed Stochastic Neighbor Embedding (tSNE)

#### Vizualization of the TSNE

#### 4.3. BlendedCNN Architecture for HSI Classification

## 5. Experimental Setups and Results

#### 5.1. Experimental Settings

#### 5.2. Performance Accuracy with Indian Pines Dataset

#### 5.3. Performance Accuracy with SalinasA Dataset

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Indian Pines dataset (

**a**) false-color composite image, (

**b**) corresponding ground-truth image with class labels.

**Figure 2.**Salinas dataset (

**a**) false-color composite image, (

**b**) corresponding ground-truth image, and (

**c**) class labels.

**Figure 9.**Classified images of Indian Pines dataset using (

**a**) NMF, (

**b**) ICA, (

**c**) RBF and cosine, (

**d**) Cosine and linear, (

**e**) KPCA with cosine kernel, and (

**f**) the proposed method.

**Figure 10.**Accuracy curves of all used DR algorithms: (

**a**) training accuracy and (

**b**) testing accuracy.

**Figure 11.**Classified images of SalinusA dataset (

**a**) ground truth and (

**b**) output using proposed method.

Sensors | Organization | No. of Bands | Wavelength Range ($\mathsf{\mu}$m) |
---|---|---|---|

AVIRIS | NASA | 224 | 0.40–2.50 |

AISA | Spectral Imaging Ltd. | 286 | 0.45–0.90 |

CASI | Itres Research | 288 | 0.43–0.87 |

PROBE-1 | Earth Search Science Inc. | 128 | 0.40–2.45 |

Layer (Type) | Output Shape | Parameter # |
---|---|---|

Input Layer | (None, 224, 224, 2, 1) | 0 |

Conv3d Layer | (None, 25, 25, 2, 1) | 0 |

Conv3d Layer | (None, 23, 23, 1, 8) | 152 |

Conv3d Layer | (None, 21, 21, 1, 16) | 1168 |

Conv2D Layer | (None, 19, 19, 64) | 9280 |

Flatten Layer | (None, 23,104) | 0 |

FCN Layer | (None, 256) | 5,914,880 |

Dropout Layer | (None, 256) | 0 |

Dense Layer | (None, 128) | 32,896 |

Dropout Layer | (None, 128) | 0 |

Output Layer | (None, 21, 21, 1, 16) |

**Table 3.**Comparison of classification performance analysis for Indian Pines dataset [43].

Recall | Precision | F1 Score | Test Loss | TA | KA | OA | AA | Time | |
---|---|---|---|---|---|---|---|---|---|

SVD [43] | 6 | 0 | 4 | 98 | 3.9 | 0 | 3.9 | 6.25 | n/a |

ICA [43] | 85 | 92 | 94 | 27.23 | 94.16 | 93.37 | 94.2 | 85.12 | n/a |

NMF [43] | 93 | 89 | 94 | 42.62 | 94.25 | 93.44 | 94.25 | 92.89 | n/a |

KPCA (Cosine) [43] | 94 | 94 | 94 | 37.82 | 93.97 | 93.13 | 93.97 | 90.69 | n/a |

KPCA (RBF) [43] | 85 | 87 | 85 | 96.82 | 85.49 | 83.35 | 85.49 | 81.36 | n/a |

MKPCA (Cosine + Linear) [43] | 96 | 96 | 96 | 23.95 | 95.75 | 95.16 | 95.76 | 89.96 | n/a |

MKPCA (Cosine + RBF) [43] | 95 | 95 | 94 | 23.75 | 94.53 | 93.75 | 94.53 | 90.73 | n/a |

SVM [60] | n/a | n/a | n/a | n/a | n/a | 85.3 | 83.10 | 79.03 | n/a |

2D-CNN [61] | n/a | n/a | n/a | n/a | n/a | 89.48 | 87.96 | 86.14 | 1.3 |

3D-CNN [62] | n/a | n/a | n/a | n/a | n/a | 91.10 | 89.98 | 91.58 | 10.6 |

Proposed | 95 | 99 | 97 | 6.2 | 98.21 | 98.10 | 98.34 | 95.21 | 4.42 |

**Table 4.**Comparison classification performance analysis for SalinasA dataset [31].

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## Share and Cite

**MDPI and ACS Style**

Hossain, M.M.; Hossain, M.A.; Musa Miah, A.S.; Okuyama, Y.; Tomioka, Y.; Shin, J.
Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network. *Electronics* **2023**, *12*, 2082.
https://doi.org/10.3390/electronics12092082

**AMA Style**

Hossain MM, Hossain MA, Musa Miah AS, Okuyama Y, Tomioka Y, Shin J.
Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network. *Electronics*. 2023; 12(9):2082.
https://doi.org/10.3390/electronics12092082

**Chicago/Turabian Style**

Hossain, Md. Moazzem, Md. Ali Hossain, Abu Saleh Musa Miah, Yuichi Okuyama, Yoichi Tomioka, and Jungpil Shin.
2023. "Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network" *Electronics* 12, no. 9: 2082.
https://doi.org/10.3390/electronics12092082