Abstract
Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral–spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification.
Highlights
Hyperspectral remote sensing technology is a focus in the remote sensing field, which has been applied for crop management, image segmentation, object recognition, etc. [1,2,3,4]
In [17], the spectrum was first partitioned into several groups and band-specific spectral–spatial features were extracted by a convolutional neural network (CNN)
We evaluated our work on three public hyperspectral datasets, Indian Pines (IP), Salinas (SA), and Pavia University (PU), captured by two different sensors: AVIRIS and ROSIS-03
Summary
Hyperspectral remote sensing technology is a focus in the remote sensing field, which has been applied for crop management, image segmentation, object recognition, etc. [1,2,3,4]. The classification of HSI is essentially to predict a specific category for each pixel according to its characteristics [5]. The traditional HSI classification methods belong to the first category, most of which analyze the HSIs and extract their shallow features for classification. The most prominent feature of HSI is the rich spectral information. Current research on HSI classification mostly uses the spectral–spatial features for classification [12,13,14,15,16,17,18,19]. In [17], the spectrum was first partitioned into several groups and band-specific spectral–spatial features were extracted by a convolutional neural network (CNN). All of this research has proven that using spectral–spatial joint features for HSI classification can effectively improve classification accuracy
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