Post Classification Using Cellular Automata for Landsat Images

2012 ◽  
Vol 433-440 ◽  
pp. 5431-5435
Author(s):  
Ebada Sarhan ◽  
Eraky Khalifa ◽  
Ayman M. Nabil
Keyword(s):  
Developing Countries ◽  
Cellular Automata ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Landsat Images ◽  
Land Cover Map ◽  
Vegetation Water ◽  
Mega Cities

The research presented in this paper aims at improving the accuracy of land-use maps produced from classification of Landsat images of mega cities in developing countries. In other words, the main objective of this paper is to find a suitable post classification technique that gives optimum results for Landsat images of mega cities in developing countries. To reach our goal, the paper presents a classification of two TM-Landsat sub scenes using a traditional statistical classifier (Maximum Likelihood) into four land cover classes (vegetation-water-Desert-Urban); then the accuracy assessment for the produced land-cover map will be calculated. Following to this step, three post processing techniques- Majority Filter, Probability label Relaxation (PLR), and Cellular Automata (CA) - will be applied in order to improve the accuracy of the previously produced land cover map. Finally, the same accuracy assessment measurements will be calculated for the two land-cover maps produced by each of the above post classification techniques. Initial results will show that CA outperformed the other techniques. In this paper we propose a methodology to implement a satellite image post classification Algorithm with cellular Automata.

scholarly journals Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Ibochi Andrew Abah ◽  
Richard jeremiah Uriah
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Image Data ◽  
Image Process ◽  
Land Use Land Cover ◽  
Error Matrix

Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

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 ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

scholarly journals Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images

2018 ◽  
Vol 8 (1) ◽  
pp. 46-62 ◽  
Author(s):  
Djelloul Mokadem ◽  
Abdelmalek Amine ◽  
Zakaria Elberrichi ◽  
David Helbert
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Urban Areas ◽  
Satellite Image ◽  
Initial Population ◽  
Landsat Images ◽  
Kohonen Maps ◽  
Multispectral Satellite Image

In this article, the detection of urban areas on satellite multispectral Landsat images. The goal is to improve the visual interpretations of images from remote sensing experts who often remain subjective. Interpretations depend deeply on the quality of segmentation which itself depends on the quality of samples. A remote sensing expert must actually prepare these samples. To enhance the segmentation process, this article proposes to use genetic algorithms to evolve the initial population of samples picked manually and get the most optimal samples. These samples will be used to train the Kohonen maps for further classification of a multispectral satellite image. Results are obtained by injecting genetic algorithms in sampling phase and this paper proves the effectiveness of the proposed approach.

scholarly journals Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

2018 ◽  
Vol 10 (8) ◽  
pp. 1192 ◽  
Author(s):  
Chen-Chieh Feng ◽  
Zhou Guo
Keyword(s):  
Land Cover ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Parameter Learning ◽  
Cloud Classification ◽  
Land Cover Map ◽  
Point Cloud Classification ◽  
Optimal Feature

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches.

scholarly journals Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

2017 ◽  
Vol 9 (1) ◽  
pp. 95 ◽  
Author(s):  
Jordi Inglada ◽  
Arthur Vincent ◽  
Marcela Arias ◽  
Benjamin Tardy ◽  
David Morin ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Map

Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map

2002 ◽  
Vol 81 (2-3) ◽  
pp. 443-455 ◽  
Author(s):  
M Laba ◽  
S.K Gregory ◽  
J Braden ◽  
D Ogurcak ◽  
E Hill ◽  
...  
Keyword(s):  
New York ◽  
Land Cover ◽  
Gap Analysis ◽  
Accuracy Assessment ◽  
Land Cover Map

scholarly journals Perubahan Penutupan Lahan Kota Bogor

2019 ◽  
Vol 2 (1) ◽  
pp. 44-51
Author(s):  
Ina Lidiawati ◽  
Ratna Sari Hasibuan ◽  
Retno Wijayanti
Keyword(s):  
Land Cover ◽  
Rice Field ◽  
Research Result ◽  
Land Cover Changes ◽  
Plantation Forest ◽  
Geospatial Information ◽  
Land Cover Map ◽  
Cover Class ◽  
The City

Pembangunan yang terjadi sangat pesat sehingga tutupan lahan di Kota Bogor berubah. Penelitian ini bertujuan untuk mengetahui tutupan lahan Kota Bogor yang berubah yaitu tahun 1996, 2006, 2016 dan  faktor-faktor yang mempengaruhi tutupan lahan Kota Bogor yang berubah tersebut. Perubahan tutupan lahan Kota Bogor dianalisis menggunakan perangkat lunak Arc.GIS 10.2. Data yang digunakan sebagai bahan analisis adalah peta tutupan lahan Kota Bogor 1996, 2006 dan 2016 dari Kementerian Lingkungan Hidup dan Kehutanan (KLHK) dan peta Rupa Bumi Indonesia (RBI). Hasil dari penelitian ini adalah klasifikasi kelas tutupan lahan hutan tanaman kota Bogor, area terbuka, pelabuhan/bandara, pemukiman/lahan, pertanian kering, pertanian kering, semak, sawah, perkebunan, dan badan air. Pada tahun 1996 tutupan lahan didominasi oleh vegetasi, semak, dan semak-semak. Perubahan tutupan lahan yang paling masif terjadi pada kelas permukiman / tanah dengan luas 6.683 hektar pada tahun 2006 dan 7.532 ha pada tahun 2016. Diperkirakan bahwa luas lahan yang akan dibangun akan terus bertambah seiring dengan pertambahan populasi. Peningkatan populasi menyebabkan lebih banyak ruang untuk perumahan dan berbagai kegiatan, selain kondisi sosial ekonomi dan arah kebijakan pemerintah yang mempengaruhi tutupan lahan kota Bogor menjadi berubah.   Development that occurred in the city of Bogor very rapidly causing land cover changes. This research purpose was to know the change of land cover of Bogor City in 1996, 2006, and 2016 and to know what factors influence the change of land cover. Changes in land cover in Bogor City were analyzed using Arc.GIS software 10.2. The data used as an analysis material were the land cover map of Bogor City 1996, 2006 and 2016 issued by the Ministry of Environment and Forestry and the map of  Rupa Bumi Indonesia issued by the Geospatial Information Agency. This research result was the classification of a land cover class of Bogor city of plantation forest, open area, port/airport, settlement/land, dry farm, dry farm, shrub, rice field, plantation, and water body. In 1996 the land cover was dominated by vegetation, shrubs, and bushes. The most massive land cover change occurred in the class of settlements/land with an area of ​​6,683 hectares in 2006 and 7,532 ha in the year 2016. It is estimated that the area of ​​land will be built will continue to grow as the population increases. The increase in population causes more space for housing and various activities, besides the socio-economic condition and the direction of government policy also affect the change of land cover in Bogor city.

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