scholarly journals Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification

2021 ◽  
Vol 767 (1) ◽  
pp. 012019
Author(s):  
M A A M Abidin ◽  
H Z M Shafri ◽  
M M A Al-Habshi ◽  
N S N Shaharum
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Learning Algorithm ◽  
State Of The Art ◽  
The State ◽  
Hyperspectral Image Classification ◽  
Deep Learning Algorithm

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

scholarly journals Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4975
Author(s):  
Fangyu Shi ◽  
Zhaodi Wang ◽  
Menghan Hu ◽  
Guangtao Zhai
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Image Classification ◽  
Large Scale ◽  
Hyperspectral Image ◽  
Image Annotation ◽  
Learning Algorithm ◽  
Magnetic Resonance Images ◽  
Biological Engineering ◽  
Hyperspectral Image Classification

Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is difficult because natural images are expensive. In addition, images in these areas are usually stored as multichannel images, such as computed tomography (CT) images, magnetic resonance images (MRI), and hyperspectral images (HSI). In this paper, we present a framework using active learning and deep learning for multichannel image classification. We use three active learning algorithms, including least confidence, margin sampling, and entropy, as the selection criteria. Based on this framework, we further introduce an “image pool” to make full advantage of images generated by data augmentation. To prove the availability of the proposed framework, we present a case study on agricultural hyperspectral image classification. The results show that the proposed framework achieves better performance compared with the deep learning model. Manual annotation of all the training sets achieves an encouraging accuracy. In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated. In practical application, the proposed framework can remarkably reduce labeling effort during the model development and upadting processes, and can be applied to multichannel image classification in agricultural and biological engineering.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

scholarly journals Low–High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification

2019 ◽  
Vol 16 (5) ◽  
pp. 776-780 ◽  
Author(s):  
Juan M. Haut ◽  
Sergio Bernabe ◽  
Mercedes E. Paoletti ◽  
Ruben Fernandez-Beltran ◽  
Antonio Plaza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Image Classification ◽  
High Power ◽  
Hyperspectral Image ◽  
Learning Models ◽  
Hyperspectral Image Classification ◽  
High Power Consumption

Hyperspectral Image Classification Algorithm Based on Principal Component Texture Feature Deep Learning

2020 ◽  
Vol 10 (9) ◽  
pp. 2027-2031
Author(s):  
Xu Yifang
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Texture Feature ◽  
Feature Learning ◽  
Principal Component ◽  
Classification Algorithm ◽  
Classification Problems ◽  
Hyperspectral Image Classification ◽  
Reconstruction Performance

Hyperspectral image classification refers to a key difficulty on the domain of remote sensing image processing. Feature learning is the basis of hyperspectral image classification problems. In addition, how to jointly use the space spectrum information is Also an important issue in hyperspectral image classification. Recent ages have seen that as further exploration is developing, the method of hyperspectral image cauterization according to deep learning has been rapidly developed. However, existing deep networks often only consider reconstruction performance while ignoring the task itself. In addition, for improving preciseness of classification, most categorization methods use the fixed-size neighborhood of per hyperspectral pixel as the object of feature extraction, ignoring the identification and difference between the neighborhood pixel and the current pixel. On the basis of exploration above, our research group put forward with an image classification algorithm based on principal component texture feature deep learning, and achieved good results.

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