Abstract
As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.
Highlights
Hyperspectral images (HSIs) have hundreds of spectral bands that comprise detailed spectral information
Our approach takes the spectral-spatial features of HSI into account for classification. It achieves a brief description of the spectral-spatial data and enhanced computational efficiency as defined: We propose a 3D-2D convolutional neural network and transfer learning model that utilizes 3D convolutions to modeling spectral-spatial information in the early network layers of the model and the 2D convolutions on top to exceptionally deal with semantic abstraction
We considered the early stopping criterion to quickly stop the training whenever the performance on the validation set detriments and ensures convergence. erefore, this pattern is factored during the training process to minimize the computation complexity without detrimental classification accuracy
Summary
Hyperspectral images (HSIs) have hundreds of spectral bands that comprise detailed spectral information. To address the abovementioned challenges, dimensionality reduction (DR) [9,10,11,12] and semisupervised classification [13, 14] approaches have been extensively adopted for HSIs. Generally, there are two classes of DR, i.e., the band selection and feature extraction [15]. Feature extraction [16,17,18,19] minimizes computational complexity by projecting high-dimensional data into low-dimensional data space and feature selection [20] picks appropriate bands from the original set of spectral bands. PCA seeks out the best orthogonal vectors for representing information from HSIs [22, 23] with minimized spectral dimension (up to 85%). Computational Intelligence and Neuroscience it improves the separability among classes, decreases, and brings a balance of the interclass and intraclass. erefore, we used PCA as an effective tool to transform the original features into a new space with reduced dimensionality and more excellent distinctive features
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