scholarly journals A Novel Analysis Dictionary Learning Model Based Hyperspectral Image Classification Method

2019 ◽  
Vol 11 (4) ◽  
pp. 397 ◽  
Author(s):  
Wei Wei ◽  
Mengting Ma ◽  
Cong Wang ◽  
Lei Zhang ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Image Classification ◽  
Dictionary Learning ◽  
Hyperspectral Image ◽  
Learning Model ◽  
Hyperspectral Data ◽  
Experimental Results ◽  
Classification Method ◽  
Strong Nonlinearity ◽  
Hyperspectral Image Classification

Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental tasks of hyperspectral data analysis. Witnessing the success of analysis dictionary learning (ADL)-based method in recent years, we propose an ADL-based supervised HSI classification method in this paper. In the proposed method, the dictionary is modeled considering both the characteristics within the spectrum and among the spectra. Specifically, to reduce the influence of strong nonlinearity within each spectrum on classification, we divide the spectrum into some segments, and based on this we propose HSI classification strategy. To preserve the relationships among spectra, similarities among pixels are introduced as constraints. Experimental results on several benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method for HSI classification.

scholarly journals Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4472
Author(s):  
Tianyu Zhang ◽  
Cuiping Shi ◽  
Diling Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Feature Maps ◽  
Spatial Feature ◽  
Global Context

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

Hyperspectral Image Classification Method Based on Adaptive Manifold Filtering

2018 ◽  
Vol 55 (4) ◽  
pp. 041010
Author(s):  
廖建尚 Liao Jianshang ◽  
王立国 Wang Liguo ◽  
郝思媛 Hao Siyuan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Method ◽  
Hyperspectral Image Classification

Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

2016 ◽  
Vol 2 (3) ◽  
pp. 703 ◽  
Author(s):  
B. Saichandana ◽  
K. Srinivas ◽  
R. KiranKumar
Keyword(s):  
Genetic Algorithm ◽  
Image Fusion ◽  
Image Classification ◽  
Empirical Mode Decomposition ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Hyperspectral Image Classification ◽  
Mode Decomposition ◽  
Fused Image

<p>Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage. 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image. This fused image is classified using genetic algorithm. Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions. This method increases classification accuracy of hyperspectral image.</p>

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