Towards Automatic Rectification Of Satellite Images Using Feature Based Matching

2005 ◽  
Author(s):  
M. Helm
Keyword(s):  
Satellite Images ◽  
Feature Based

Feature Based Registration of Satellite Images

2007 ◽  
Author(s):  
Y. Bentoutou ◽  
N. Taleb ◽  
A. Bounoua ◽  
K. Kpalma ◽  
J. Ronsin
Keyword(s):  
Satellite Images ◽  
Feature Based

scholarly journals COMPARISON OF PIXEL-BASED AND FEATURE-BASED APPROACH FOR SMALL OBJECT CHANGE DETECTION

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 353-357
Author(s):  
J. Seo ◽  
T. Kim
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Rate Of Change ◽  
Image Resolution ◽  
Detection Methods ◽  
Small Object ◽  
Feature Points ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Feature Based

Abstract. Satellite image resolution has evolved to daily revisit and sub-meter GSD. Main targets of previous remote sensing were forest, vegetation, damage area by disasters, land use and land cover. Developments in satellite images have brought expectations on more sophisticated and various change detection of objects. Accordingly, we focused on unsupervised change detection of small objects, such as vehicles and ships. In this paper, existing change detection methods were applied to analyze their performances for pixel-based and feature-based change of small objects. We used KOMPSAT-3A images for tests. Firstly, we applied two change detection algorithms, MAD and IR-MAD, which are most well-known pixel-based change detection algorithms, to the images. We created a change magnitude map using the change detection methods. Thresholding was applied to determine change and non-change pixels. Next, the satellite images were transformed as 8-bit images for extracting feature points. We extracted feature points using SIFT and SURF methods to analyze feature-based change detection. We assumed to remove false alarms by eliminating feature points of non-changed objects. Therefore, we applied a feature-based matcher and matched feature points on identical image locations were eliminated. We used non-matched feature points for change/non-change analysis. We observed changes by creating a 5x5 size ROI around extracted feature points in the change/non-change map. We determined that change has occurred on feature points if the rate of change pixels with ROI was more than 50%. We analyzed the performance of pixel-based and feature-based change detection using ground truths. The F1-score, AUC value, and ROC were used to compare the performance of change detection. Performance showed that feature-based approaches performed better than pixel-based approaches.

NEURO-WAVELET CLASSIFIER FOR MULTISPECTRAL REMOTE SENSING IMAGES

2007 ◽  
Vol 05 (04) ◽  
pp. 589-611 ◽  
Author(s):  
B. UMA SHANKAR ◽  
SAROJ K. MEHER ◽  
ASHISH GHOSH
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Satellite Images ◽  
Satellite Image ◽  
Spectral Feature ◽  
Spectral Features ◽  
Remote Sensing Images ◽  
Feature Based ◽  
Multispectral Remote Sensing

A neuro-wavelet supervised classifier is proposed for land cover classification of multispectral remote sensing images. Features extracted from the original pixels information using wavelet transform (WT) are fed as input to a feed forward multi-layer neural network (MLP). The WT basically provides the spatial and spectral features of a pixel along with its neighbors and these features are used for improved classification. For testing the performance of the proposed method, we have used two IRS-1A satellite images and one SPOT satellite image. Results are compared with those of the original spectral feature based classifiers and found to be consistently better. Simulation study revealed that Biorthogonal 3.3 (Bior3.3) wavelet in combination with MLP performed better compared to all other wavelets. Results are evaluated visually and quantitatively with two measurements, β index of homogeneity and Davies–Bouldin (DB) index for compactness and separability of classes. We suggested a modified β index in accessing the percentage of accuracy (PAβ) of the classified images also.

A Hybrid Dense Matching Method for Satellite Images

2013 ◽  
Vol 726-731 ◽  
pp. 4547-4551
Author(s):  
Sheng Hua Teng ◽  
Ning Yang
Keyword(s):  
Satellite Images ◽  
Matching Problem ◽  
Canny Operator ◽  
Dense Matching ◽  
Edge Points ◽  
Feature Based ◽  
Matching Element ◽  
Grid Points ◽  
Geometrically Constrained ◽  
Image Pairs

To solve the dense matching problem for stereoscopic satellite images, a hybrid matching scheme integrating multiple methods is proposed. This scheme utilizes two types of matching element including grid points and edge points. First the geometrically constrained cross-correlation (GC3) method is used to extract matching grids. While in less textured regions using GC3 cannot get sufficient matching grids, so the local affine transformation is used to establish the region correspondence, and more matching grids can be generated. Edges are extracted by Canny operator and approximated with a series of straight edge segments using a polygon approximation. Based on these approximated edges, edge correspondences between image pairs are established using GC3. This scheme fuses the region based and the feature based matching methods. It has been tested with real satellite images and the results demonstrate its accuracy and efficiency.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

Sign in / Sign up

Export Citation Format

Share Document