Abstract
Convolutional neural network (CNN) has shown excellent performance in hyperspectral image (HSI) classification. However, the structure of the CNN models is complex, requiring many training parameters and floating-point operations (FLOPs). This is often inefficient and results in longer training and testing time. In addition, the label samples of hyperspectral data are limited, and a deep network often causes the over-fitting phenomenon. Hence, a dual-path small convolution (DPSC) module is proposed. It is composed of two 1 × 1 small convolutions with a residual path and a density path. It can effectively extract abstract features from HSI. A dual-path small convolution network (DPSCN) is constructed by stacking DPSC modules. Specifically, the proposed model uses a DPSC module to complete the extraction of spectral and spectral–spatial features successively. It then uses a global average pooling layer at the end of the model to replace the conventional fully connected layer to complete the final classification. In the implemented study, all convolutional layers of the proposed network, except the middle layer, use 1 × 1 small convolution, effectively reduced model parameters and increased the speed of feature extraction processes. DPSCN was compared with several current state-of-the-art models. The results on three benchmark HSI data sets demonstrated that the proposed model is of lower complexity, has stronger generalization ability, and has higher classification efficiency.
Highlights
For solving the problems mentioned above and being inspired by literature [28], we proposed a dual-path small convolution network (DPSCN) with successive feature learning blocks and 2D filters
Three commonly used metrics were used to evaluate the performance of different models, i.e., Overall Accuracy (OA), Average Accuracy (AA), and Kappa coefficient (Kappa)
The overall accuracy (OA) represents the fraction of test samples that are differentiated correctly, i.e., n where N denotes the total number of samples, n is the number of land cover classes, and Hi represents the number of samples correctly classified
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Hyperspectral remote sensing can collect electromagnetic spectrum in the wavelength, ranging from visible light to near-infrared. It allows the HSI to have plenty of continuous spectral information and geometric space information of the target object. Hyperspectral remote sensing has been widely used in land monitoring [1,2], land vegetation identification [3], and lake water quality detection [4]. Image classification has always been a core and fundamental part of the application of hyperspectral remote sensing. The quality of classification results directly affects the subsequent analytical processes. It is very crucial to construct an accurate and efficient HSI classification model to ensure high-quality image classification
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