A Morphological Neural Network Approach for Vehicle Detection from High Resolution Satellite Imagery

2006 ◽  
pp. 99-106 ◽  
Author(s):  
Hong Zheng ◽  
Li Pan ◽  
Li Li
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Imagery ◽  
Vehicle Detection ◽  
Network Approach ◽  
Neural Network Approach ◽  
High Resolution Satellite Imagery

scholarly journals Vehicle Detection and Classification from High Resolution Satellite Images

2014 ◽  
Vol II-1 ◽  
pp. 1-8 ◽  
Author(s):  
L. Abraham ◽  
M. Sasikumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Vehicle Detection ◽  
Satellite Imaging ◽  
Suitable Alternative ◽  
High Resolution Satellite Images ◽  
Technological Developments ◽  
High Resolution Satellite Imagery

In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

scholarly journals Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

2020 ◽  
Vol 12 (5) ◽  
pp. 789 ◽  
Author(s):  
Kun Li ◽  
Xiangyun Hu ◽  
Huiwei Jiang ◽  
Zhen Shu ◽  
Mi Zhang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Imagery ◽  
Conditional Random Field ◽  
Geodesic Distance ◽  
Feature Maps ◽  
Object Boundary ◽  
Multi Scale ◽  
High Resolution Satellite Imagery ◽  
Fully Connected

Automatic extraction of region objects from high-resolution satellite imagery presents a great challenge, because there may be very large variations of the objects in terms of their size, texture, shape, and contextual complexity in the image. To handle these issues, we present a novel, deep-learning-based approach to interactively extract non-artificial region objects, such as water bodies, woodland, farmland, etc., from high-resolution satellite imagery. First, our algorithm transforms user-provided positive and negative clicks or scribbles into guidance maps, which consist of a relevance map modified from Euclidean distance maps, two geodesic distance maps (for positive and negative, respectively), and a sampling map. Then, feature maps are extracted by applying a VGG convolutional neural network pre-trained on the ImageNet dataset to the image X, and they are then upsampled to the resolution of X. Image X, guidance maps, and feature maps are integrated as the input tensor. We feed the proposed attention-guided, multi-scale segmentation neural network (AGMSSeg-Net) with the input tensor above to obtain the mask that assigns a binary label to each pixel. After a post-processing operation based on a fully connected Conditional Random Field (CRF), we extract the selected object boundary from the segmentation result. Experiments were conducted on two typical datasets with diverse region object types from complex scenes. The results demonstrate the effectiveness of the proposed method, and our approach outperforms existing methods for interactive image segmentation.

Self-Supervised Deep Learning for Vehicle Detection in High-Resolution Satellite Imagery

2021 ◽  
Author(s):  
Zeyad Awwad ◽  
Faisal Alnasser ◽  
Tariq Alshahrani ◽  
Matthew Moraguez ◽  
Ahmad Alabdulkareem ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Vehicle Detection ◽  
High Resolution Satellite Imagery

Robust Ultra-High Resolution Microwave Planar Sensor Using Fuzzy Neural Network Approach

2017 ◽  
Vol 17 (2) ◽  
pp. 323-332 ◽  
Author(s):  
Mohammad Abdolrazzaghi ◽  
Mohammad Hossein Zarifi ◽  
Witold Pedrycz ◽  
Mojgan Daneshmand
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Fuzzy Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural

scholarly journals Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model

2020 ◽  
Vol 10 (2) ◽  
pp. 602 ◽  
Author(s):  
Min Ji ◽  
Lanfa Liu ◽  
Rongchun Zhang ◽  
Manfred F. Buchroithner
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Imagery ◽  
Human Life ◽  
Haiti Earthquake ◽  
Intermediate Layers ◽  
High Resolution Satellite Imagery ◽  
Collapsed Buildings ◽  
Very High ◽  
Visual Geometry

The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery.

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