EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2016 ◽

Vol XLI-B7 ◽

pp. 83-89 ◽

Cited By ~ 1

Author(s):

Pai-Hui Hsu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Feature Extraction ◽

Image Classification ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Training Phase ◽

Neural Network Classifier ◽

Hyperspectral Image Classification ◽

Wavelet Networks

The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.

Download Full-text

Fast Sequential Feature Extraction for Recurrent Neural Network-Based Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3018449 ◽

2020 ◽

pp. 1-18

Author(s):

Andong Ma ◽

Anthony M. Filippi ◽

Zhangyang Wang ◽

Zhengcong Yin ◽

Da Huo ◽

...

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Image Classification ◽

Recurrent Neural Network ◽

Hyperspectral Image ◽

Hyperspectral Image Classification

Download Full-text

Multi-scale guided feature extraction and classification algorithm for hyperspectral images

Scientific Reports ◽

10.1038/s41598-021-97636-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shiqi Huang ◽

Ying Lu ◽

Wenqing Wang ◽

Ke Sun

Keyword(s):

Feature Extraction ◽

Image Classification ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Image Data ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Multi Scale ◽

Hyperspectral Image Data ◽

Guided Filtering

AbstractTo solve the problem that the traditional hyperspectral image classification method cannot effectively distinguish the boundary of objects with a single scale feature, which leads to low classification accuracy, this paper introduces the idea of guided filtering into hyperspectral image classification, and then proposes a multi-scale guided feature extraction and classification (MGFEC) algorithm for hyperspectral images. Firstly, the principal component analysis theory is used to reduce the dimension of hyperspectral image data. Then, guided filtering algorithm is used to achieve multi-scale spatial structure extraction of hyperspectral image by setting different sizes of filtering windows, so as to retain more edge details. Finally, the extracted multi-scale features are input into the support vector machine classifier for classification. Several practical hyperspectral image datasets were used to verify the experiment, and compared with other spectral feature extraction algorithms. The experimental results show that the multi-scale features extracted by the MGFEC algorithm proposed in this paper are more accurate than those extracted by only using spectral information, which leads to the improvement of the final classification accuracy. This fully shows that the proposed method is not only effective, but also suitable for processing different hyperspectral image data.

Download Full-text

Spectral-Spatial Feature Extraction Using PCA and Multi-Scale Deep Convolutional Neural Network for Hyperspectral Image Classification

2019 22nd International Conference on Computer and Information Technology (ICCIT) ◽

10.1109/iccit48885.2019.9038385 ◽

2019 ◽

Cited By ~ 1

Author(s):

Md. Rakibul Haque ◽

Sadia Zaman Mishu

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Image Classification ◽

Hyperspectral Image ◽

Deep Convolutional Neural Network ◽

Hyperspectral Image Classification ◽

Spatial Feature ◽

Multi Scale

Download Full-text

Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks

Remote Sensing ◽

10.3390/rs11020194 ◽

2019 ◽

Vol 11 (2) ◽

pp. 194 ◽

Cited By ~ 8

Author(s):

Andong Ma ◽

Anthony Filippi ◽

Zhangyang Wang ◽

Zhengcong Yin

Keyword(s):

Neural Networks ◽

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Block Matching ◽

Hyperspectral Images ◽

Distance Measures ◽

Single Image ◽

Hyperspectral Image Classification ◽

Spectral Angle

Classification is a common objective when analyzing hyperspectral images, where each pixel is assigned to a predefined label. Deep learning-based algorithms have been introduced in the remote-sensing community successfully in the past decade and have achieved significant performance improvements compared with conventional models. However, research on the extraction of sequential features utilizing a single image, instead of multi-temporal images still needs to be further investigated. In this paper, a novel strategy for constructing sequential features from a single image in long short-term memory (LSTM) is proposed. Two pixel-wise-based similarity measurements, including pixel-matching (PM) and block-matching (BM), are employed for the selection of sequence candidates from the whole image. Then, the sequential structure of a given pixel can be constructed as the input of LSTM by utilizing the first several matching pixels with high similarities. The resulting PM-based LSTM and BM-based LSTM are appealing, as all pixels in the whole image are taken into consideration when calculating the similarity. In addition, BM-based LSTM also utilizes local spectral-spatial information that has already shown its effectiveness in hyperspectral image classification. Two common distance measures, Euclidean distance and spectral angle mapping, are also investigated in this paper. Experiments with two benchmark hyperspectral images demonstrate that the proposed methods achieve marked improvements in classification performance relative to the other state-of-the-art methods considered. For instance, the highest overall accuracy achieved on the Pavia University image is 96.20% (using both BM-based LSTM and spectral angle mapping), which is an improvement compared with 84.45% overall accuracy generated by 1D convolutional neural networks.

Download Full-text