In recent years, deep learning-based hyperspectral image classification techniques have developed rapidly. Many effective deep learning models have been proposed in academia, such as 3D-CNN and some other CNN-based methods, which have achieved high accuracy in hyperspectral image classification. These excellent methods rely on large number of labeled samples for their effectiveness. In practice, labeling pixels of hyperspectral images is expensive (time-consuming and labor-intensive), so it is often difficult to obtain enough labeled samples for training deep neural network models. To address this problem, we propose a multiscale attention-based few-shot learning (MAFSL) method using only a few labeled samples for each category in this paper. First, few-shot learning is performed on mini-ImageNet to obtain prior knowledge, and then the knowledge is transferred to the hyperspectral dataset. Before embedding features, multiscale attention-based feature extraction with reconstruction loss is applied to the hyperspectral image. Then, the obtained features are input into the spatial feature extraction network and the spectral extraction network, respectively. Finally, the embedded features are put into the metric space for classification. The proposed model can get a higher classification accuracy because the extracted features have less correlation with each other. Experimental results show that our MAFSL outperforms many existing supervised learning methods when only a small number of labeled samples are used.
KEYWORDS: Education and training, Convolution, Feature extraction, 3D modeling, Data modeling, Machine learning, Deep learning, Overfitting, Hyperspectral imaging, Image classification
Recently, using convolutional neural networks (CNNs) to extract spectral-spatial features has become an effective way for HSI classification. However, complex CNN models require many training parameters and floating-point operations (FLOPs). This usually means longer training and testing times. Furthermore, deep networks become prone to overfitting when the labeled samples of hyperspectral data are limited. In this article, a lightweight convolution network with selfknowledge distillation (SKDLCN) is developed for HSI classification, and it has two crucial elements, including a dualpath convolution network and a self-knowledge distillation module. At first, a method called 3-D transformation is performed for data augmentation to alleviate the overfitting problem. Then, the proposed network consists of small 1 × 1 convolutions with a residual path and a density path. Specifically, it can efficiently complete the extraction of spectral and spectral-spatial features sequentially from HSI. Self-knowledge distillation can be explained within the knowledge distillation framework as students become teachers, which gradually extracts knowledge of the model itself during training. Specifically, the target is adaptively adjusted by combining the ground truth of the model itself and past predictions. Experiments on two public HSI datasets demonstrate that the proposed method is significantly superior to some state-ofthe- art methods with limited training samples.
Hyperspectral Image (HSI) classification aims to assign each hyperspectral pixel with an appropriate land-cover category. In recent years, deep learning (DL) has received attention from a growing number of researchers. Hyperspectral image classification methods based on DL have shown admirable performance, but there is still room for improvement in terms of exploratory capabilities in spatial and spectral dimensions. To improve classification accuracy and reduce training samples, we propose a double branch attention network (OCDAN) based on 3-D octave convolution and dense block. Especially, we first use a 3-D octave convolution model and dense block to extract spatial features and spectral features respectively. Furthermore, a spatial attention module and a spectral attention module are implemented to highlight more discriminative information. Then the extracted features are fused for classification. Compared with the state-of-the-art methods, the proposed framework can achieve superior performance on two hyperspectral datasets, especially when the training samples are signally lacking. In addition, ablation experiments are utilized to validate the role of each part of the network.
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