Segmentation of Satellite Images Using Contractive Autoencoder (CAE) Aided Deep Learning Approach

2021 ◽  
pp. 667-675
Author(s):  
Manami Barthakur ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
Learning Approach

A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images

2020 ◽  
Vol 160 ◽  
pp. 260-274 ◽  
Author(s):  
Miguel M. Pinto ◽  
Renata Libonati ◽  
Ricardo M. Trigo ◽  
Isabel F. Trigo ◽  
Carlos C. DaCamara
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
Learning Approach ◽  
Burned Areas ◽  
Temporal Sequences

scholarly journals Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models

2020 ◽  
Vol 12 (10) ◽  
pp. 1581 ◽  
Author(s):  
Daniel Perez ◽  
Kazi Islam ◽  
Victoria Hill ◽  
Richard Zimmerman ◽  
Blake Schaeffer ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Satellite Images ◽  
Support Vector ◽  
Learning Approach ◽  
Learning Models ◽  
Learning Techniques ◽  
The World ◽  
New Locations

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.

scholarly journals DEEP LEARNING APPROACH FOR URBAN MAPPING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 261-266
Author(s):  
C. Najjaj ◽  
H. Rhinane ◽  
A. Hilali
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
Learning Approach ◽  
Building Detection ◽  
Building Extraction ◽  
Great Precision ◽  
Proposed Model ◽  
Weight Model

Abstract. Researchers in computer vision and machine learning are becoming increasingly interested in image semantic segmentation. Many methods based on convolutional neural networks (CNNs) have been proposed and have made considerable progress in the building extraction mission. This other methods can result in suboptimal segmentation outcomes. Recently, to extract buildings with a great precision, we propose a model which can recognize all the buildings and present them in mask with white and the other classes in black. This developed network, which is based on U-Net, will boost the model's sensitivity. This paper provides a deep learning approach for building detection on satellite imagery applied in Casablanca city, Firstly, to begin we describe the terminology of this field. Next, the main datasets exposed in this project which’s 1000 satellite imagery. Then, we train the model UNET for 25 epochs on the training and validation datasets and testing the pretrained weight model with some unseen satellite images. Finally, the experimental results show that the proposed model offers good performance obtained as a binary mask that extract all the buildings in the region of Casablanca with a higher accuracy and entirety to achieve an average F1 score on test data of 0.91.

scholarly journals Satellite images and machine learning can identify remote communities to facilitate access to health services

2019 ◽  
Vol 26 (8-9) ◽  
pp. 806-812 ◽  
Author(s):  
Emilie Bruzelius ◽  
Matthew Le ◽  
Avi Kenny ◽  
Jordan Downey ◽  
Matteo Danieletto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Community Health ◽  
Satellite Images ◽  
Health Workers ◽  
Scale Up ◽  
Healthcare Delivery ◽  
Accurate Information ◽  
Learning Approach ◽  
Remote Communities

Abstract Objective Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning. Materials and Methods We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trained an algorithm to detect individual buildings, then examined building clusters to identify groupings suggestive of communities. The approach was validated in southeastern Liberia, by comparing algorithmically generated results with community location data collected manually by enumerators and community health workers. Results The deep learning approach achieved 86.47% positive predictive value and 79.49% sensitivity with respect to individual building detection. The approach identified 75.67% (n = 451) of communities registered through the community enumeration process, and identified an additional 167 potential communities not previously registered. Several instances of false positives and false negatives were identified. Discussion Analysis of satellite images is a promising solution for mapping remote communities rapidly, and with relatively low costs. Further research is needed to determine whether the communities identified algorithmically, but not registered in the manual enumeration process, are currently inhabited. Conclusions To our knowledge, this study represents the first effort to apply image recognition algorithms to rural healthcare delivery. Results suggest that these methods have the potential to enhance community health worker scale-up efforts in underserved remote communities.

A Multiclass Deep Learning Approach for LULC Classification of Multispectral Satellite Images

2020 ◽  
Author(s):  
Dinesh Sathyanarayanan ◽  
DV Anudeep ◽  
C Anjana Keshav Das ◽  
Sanat Bhanadarkar ◽  
Uma D ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
Learning Approach ◽  
Multispectral Satellite Images

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach

Deep Learning Approach for the Detection of Depression in Twitter

2019 ◽  
Author(s):  
Aswathy K S ◽  
Rafeeque P C ◽  
Reena Murali
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Detection Of Depression
Sign in / Sign up

Export Citation Format

Share Document