scholarly journals Quantitative Analysis of Tsunami Debris by Object-Based Image Classification of the Aerial Photo and Satellite Image

2012 ◽  
Vol 68 (2) ◽  
pp. I_371-I_375
Author(s):  
Takumi FUKUOKA ◽  
Shunichi KOSHIMURA
Keyword(s):  
Quantitative Analysis ◽  
Image Classification ◽  
Satellite Image ◽  
Aerial Photo ◽  
Object Based

scholarly journals Object-Based and Rule-Based Classification of Synthetic Aperture Radar Images

2019 ◽  
Vol 8 (10) ◽  
pp. 2458-2463
Keyword(s):  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Accurate Information ◽  
Barren Land ◽  
Radar Images ◽  
Satellite Image Classification ◽  
Object Based ◽  
Terrain Features

Since thousands of years, the land is the basic and very important requirement for humans to survive and grow. The surface area of the earth provided by nature contains many different geographical locations divided into oceans, mountains, rivers, barren land, fertile land, ice caps and many more. The huge land masses and water bodies need to be observed and analyzed for optimum utilization of resources. Remote sensing is the best possible way to observe the earth's surface from a distance through different satellites and sensors. But most of the satellite images are not clear up to the extent to classify different terrain features accurately. Hence classification of image is needed to observe different terrain features in original images. In this study, the aim is to propose a branch of natural computation for SAR image classification into different terrain features with better information retrieval and accuracy measures as compared to traditional methods for satellite image classification. The object-based analysis has been used to extract spectral reflectance of five texture measures namely urban, rocky, vegetation, water and barren to generate training set. Minimum distance to mean classifier has been used with one of the Nature Inspired computation technique i.e. bacterial foraging optimization algorithm for the satellite image classification, to extract the more accurate information about land area of Alwar district, Rajasthan, India. In the proposed study a high-quality thematic map has been generated with the 7-band multi-spectral, medium-resolution satellite images. This approach provides the greater speed and accuracy in its computation with 97.43% overall accuracy (OA) and 0.96 Kappa co-efficient.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

scholarly journals Artificial Neural Networks for Satellite Image Classification of Shoreline Extraction for Land and Water Classes of the North West Coast of Peninsular Malaysia

2018 ◽  
Vol 24 (2) ◽  
pp. 1382-1387 ◽  
Author(s):  
Syaifulnizam Abd Manaf ◽  
Norwati Mustapha ◽  
Md. Nasir Sulaiman ◽  
Nor Azura Husin ◽  
Mohd Radzi Abdul Hamid
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Image Classification ◽  
West Coast ◽  
Satellite Image ◽  
Peninsular Malaysia ◽  
North West ◽  
The North ◽  
Artificial Neural

scholarly journals Object-Based Image Classification of Summer Crops with Machine Learning Methods

2014 ◽  
Vol 6 (6) ◽  
pp. 5019-5041 ◽  
Author(s):  
José Peña ◽  
Pedro Gutiérrez ◽  
César Hervás-Martínez ◽  
Johan Six ◽  
Richard Plant ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Object Based

scholarly journals Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

2013 ◽  
Vol 79 (5) ◽  
pp. 433-440 ◽  
Author(s):  
Sory I. Toure ◽  
Douglas A. Stow ◽  
John R. Weeks ◽  
Sunil Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Curve Matching ◽  
Object Based

scholarly journals Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province

AGRIFOR ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Agus Sofyan
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Aerial Photo ◽  
Recording Time ◽  
Object Based Image Analysis ◽  
Optical Sensitivity ◽  
East Kalimantan ◽  
Object Based ◽  
Open Land

Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.

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