Analyzing the effects of pixel-scale data fusion in hyperspectral image classification performance

2020 ◽  
Author(s):  
Michael Alvarez ◽  
Carlos A. Theran ◽  
Emmanuel Arzuaga ◽  
Heidy Sierra
Keyword(s):  
Data Fusion ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Image Classification ◽  
Pixel Scale ◽  
Scale Data

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.

scholarly journals A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 443-449
Author(s):  
P. Zhong ◽  
Z. Q. Gong ◽  
C. Schönlieb
Keyword(s):  
Image Classification ◽  
Real World ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Fine Tuning ◽  
Hyperspectral Image Classification ◽  
Latent Factors ◽  
Deep Model ◽  
Training Samples ◽  
Limited Training Samples

In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons), which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.

scholarly journals Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0188996 ◽  
Author(s):  
Muhammad Ahmad ◽  
Stanislav Protasov ◽  
Adil Mehmood Khan ◽  
Rasheed Hussain ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Image Classification ◽  
Learning Framework

scholarly journals A 3D-2D Multibranch Feature Fusion and Dense Attention Network for Hyperspectral Image Classification

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1271
Author(s):  
Hongmin Gao ◽  
Yiyan Zhang ◽  
Yunfei Zhang ◽  
Zhonghao Chen ◽  
Chenming Li ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Receptive Fields ◽  
Classification Performance ◽  
Redundant Information ◽  
Hyperspectral Image Classification ◽  
Training Process ◽  
Attention Network ◽  
Spatial Features

In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.

scholarly journals Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3131
Author(s):  
Zhongwei Li ◽  
Xue Zhu ◽  
Ziqi Xin ◽  
Fangming Guo ◽  
Xingshuai Cui ◽  
...  
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Generative Adversarial Networks ◽  
Hyperspectral Image Classification ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Spatial Features ◽  
Virtual Samples

Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguous, and GANs are prone to the mode collapse, which lead the poor generalization abilities ultimately. Moreover, most of these models only consider the extraction of spectral or spatial features. They fail to combine the two branches interactively and ignore the correlation between them. Consequently, the variational generative adversarial network with crossed spatial and spectral interactions (CSSVGAN) was proposed in this paper, which includes a dual-branch variational Encoder to map spectral and spatial information to different latent spaces, a crossed interactive Generator to improve the quality of generated virtual samples, and a Discriminator stuck with a classifier to enhance the classification performance. Combining these three subnetworks, the proposed CSSVGAN achieves excellent classification by ensuring the diversity and interacting spectral and spatial features in a crossed manner. The superior experimental results on three datasets verify the effectiveness of this method.

scholarly journals A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification

2021 ◽  
Vol 13 (2) ◽  
pp. 193
Author(s):  
Ziping He ◽  
Kewen Xia ◽  
Tiejun Li ◽  
Baokai Zu ◽  
Zhixian Yin ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Model Performance ◽  
Classification Performance ◽  
Label Propagation ◽  
Hyperspectral Image Classification ◽  
Cooperation And Competition ◽  
Cooperation Mechanism ◽  
Highly Correlated ◽  
Public Datasets

Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent research indicates that the classification performance might be deteriorated by the unlabeled samples. Here, we proposed a novel graph-based semi-supervised algorithm combined with particle cooperation and competition, which can improve the model performance effectively by using unlabeled samples. First, for the purpose of reducing the generation of label noise, we used an efficient constrained graph construction approach to calculate the affinity matrix, which is capable of constructing a highly correlated similarity relationship between the graph and the samples. Then, we introduced a particle competition and cooperation mechanism into label propagation, which could detect and re-label misclassified samples dynamically, thus stopping the propagation of wrong labels and allowing the overall model to obtain better classification performance by using predicted labeled samples. Finally, we applied the proposed model into hyperspectral image classification. The experiments used three real hyperspectral datasets to verify and evaluate the performance of our proposal. From the obtained results on three public datasets, our proposal shows great hyperspectral image classification performance when compared to traditional graph-based SSL algorithms.

scholarly journals A Lightweight 1-D Convolution Augmented Transformer with Metric Learning for Hyperspectral Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1751
Author(s):  
Xiang Hu ◽  
Wenjing Yang ◽  
Hao Wen ◽  
Yu Liu ◽  
Yuanxi Peng
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Hyperspectral Image ◽  
Metric Learning ◽  
Image Data ◽  
Activation Function ◽  
Classification Performance ◽  
Hyperspectral Image Classification

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time.

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