In recent years, deep learning methods have been widely applied to hyperspectral image classification. However, these deep learning methods need lots of training samples to tune abundant parameters which induce a heavy computation burden. In this paper, we propose a classification model based on spectral spatial feature extraction and deep rotation forest ensemble with AdaBoost (SSDRA). First, linear discriminant analysis (LDA) and extended morphological attribute profile (EMAP) are used to extract features from hyperspectral images. In this way, the useful features of hyperspectral images can be integrated to a great extent while reducing the dimension of hyperspectral images. Then, the features of joint regions combining patches and superpixels are input into the classification model for training. Next, a deep rotation forest ensemble with AdaBoost (DRA) is designed for classification, so that our method can achieve superior performance with a small number of training samples. Finally, to optimize the classification results, superpixel smoothing is performed. The final results are obtained by using majority voting on the classification results within superpixels and among superpixels of different scales. To verify the effectiveness of the proposed method, experiments are performed using two public hyperspectral datasets. The experimental results demonstrate that the proposed method achieves satisfactory classification results.
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.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.