Ship Image Classification Using Deep Learning Method

2020 ◽  
pp. 220-227
Author(s):  
Pratik Patil ◽  
Mohanasundaram Ranganathan ◽  
Hemprasad Patil
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Learning Method

scholarly journals Towards Image Classification with Machine Learning Methodologies for Smartphones

2019 ◽  
Vol 1 (4) ◽  
pp. 1039-1057 ◽  
Author(s):  
Lili Zhu ◽  
Petros Spachos
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Time Constraints ◽  
Learning Method ◽  
Android Application ◽  
Optimal Model ◽  
Recent Developments ◽  
Learning Architectures

Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery.

scholarly journals Contrastive Learning Based on Transformer for Hyperspectral Image Classification

2021 ◽  
Vol 11 (18) ◽  
pp. 8670
Author(s):  
Xiang Hu ◽  
Teng Li ◽  
Tong Zhou ◽  
Yu Liu ◽  
Yuanxi Peng
Keyword(s):  
Deep Learning ◽  
Human Resources ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Image Features ◽  
Hyperspectral Data ◽  
Learning Method ◽  
Hyperspectral Image Classification ◽  
Excellent Performance ◽  
Transformer Model

Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability of labeled data is limited due to the significant human resources and time costs of labeling hyperspectral data. Unsupervised learning for hyperspectral image classification has thus received increasing attention. In this paper, we propose a novel unsupervised framework based on a contrastive learning method and a transformer model for hyperspectral image classification. The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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