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

Abstract

The ample amount of information from hyperspectral image (HSI) bands allows the non-destructive detection and recognition of earth objects. However, dimensionality reduction (DR) of hyperspectral images (HSI) is required before classification as the classifier may suffer from the curse of dimensionality. Therefore, dimensionality reduction plays a significant role in HSI data analysis (e.g., effective processing and seamless interpretation). In this article, a sophisticated technique established as t-Distributed Stochastic Neighbor Embedding (tSNE) following the dimension reduction along with a blended CNN was implemented to improve the visualization and characterization of HSI. In the procedure, first, we employed principal component analysis (PCA) to reduce the HSI dimensions and remove non-linear consistency features between the wavelengths to project them to a smaller scale. Then we proposed tSNE to preserve the local and global pixel relationships and check the HSI information visually and experimentally. Lastly, it yielded two-dimensional data, improving the visualization and classification accuracy compared to other standard dimensionality-reduction algorithms. Finally, we employed deep-learning-based CNN to classify the reduced and improved HSI intra- and inter-band relationship-feature vector. The evaluation performance of 95.21% accuracy and 6.2% test loss proved the superiority of the proposed model compared to other state-of-the-art DR reduction algorithms.

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models

“…This is done by using a Gaussian kernel to smooth the distances between the points. The probability of two points being neighbors is then calculated as in Eq 7 [ 42 ]. where x i , x j are the two points, || x i − x j || is the distance between the two points, and sigma is a parameter that controls the bandwidth of the Gaussian kernel.…”

“…This is done by using a Gaussian kernel to smooth the distances between the points. The probability of two points being neighbors is then calculated as in Eq 7 [42].…”

Loop Closure Detection Based on Compressed ConvNet Features in Dynamic Environments

“…They decomposed and reconstructed the similarity matrix to address the issue of perceptual aliasing in loop closure detection. Hossain, M et al [30] combined Principal Component Analysis (PCA) with convolutional neural networks to reduce the dimensionality of hyperspectral images, eliminating non-linear consistency features between wavelengths and improving the accuracy of visualization and classification. However, autoencoders and PCA have limitations in terms of understanding the data, and when dealing with images in complex environments, they may lead to the loss of important data.…”