scholarly journals Land Use and Land Cover Classification Using Deep Belief Network for LISS-III Multispectral Satellite Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 94-98
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Multispectral Images ◽  
Deep Belief Networks ◽  
Learning Techniques ◽  
Multispectral Satellite Images ◽  
Liss Iii ◽  
High Level

Land Use and Land Cover (LULC) classification is one of the familiar applications of geographical monitoring. Deep learning techniques like deep belief networks (DBN), are used for the purpose of feature extraction and classification of multispectral images. In this proposed framework, by applying DBN, spatial and spectral features were extracted and classified with high level of classification accuracy. LISS III images of Kottayam district, Kerala were used as experimental images. This proposed framework proved that, DBN has a high ability to extract the feature and classify the multispectral images with high accuracy than traditional methods.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

2019 ◽  
Vol 11 (13) ◽  
pp. 1600 ◽  
Author(s):  
Flávio F. Camargo ◽  
Edson E. Sano ◽  
Cláudia M. Almeida ◽  
José C. Mura ◽  
Tati Almeida
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Tropical Savanna ◽  
Learning Techniques ◽  
Bare Soils

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman–Durden, Yamaguchi, and Cloude–Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.

scholarly journals Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario

2015 ◽  
Vol 57 ◽  
Author(s):  
Matteo Picchiani ◽  
Marco Chini ◽  
Stefano Corradini ◽  
Luca Merucci ◽  
Alessandro Piscini ◽  
...  
Keyword(s):  
Neural Network ◽  
Volcanic Ash ◽  
Satellite Images ◽  
Test Cases ◽  
Multispectral Images ◽  
The Neural Network ◽  
Multispectral Satellite Images ◽  
Potential Use

<div class="WordSection1"><div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event. </span></p></div></div></div><p><em><br /></em></p><p><em><br /></em></p></div><em><br clear="all" /></em>

Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers

2017 ◽  
Vol 7 (3) ◽  
pp. 1180
Author(s):  
S.V.S. Prasad ◽  
T. Satya Savithri ◽  
Iyyanki V. Murali Krishna
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Satellite Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Landsat 8 ◽  
Hybrid Technique ◽  
Multispectral Satellite Images ◽  
Artificial Neural Network Ann

<p>The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images.</p>

scholarly journals CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1585-1591
Author(s):  
M. S. Mondal ◽  
N. Sharma ◽  
M. Kappas ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficient ◽  
Satellite Images ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Land Cover Map ◽  
Liss Iii

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

scholarly journals Detection of Land Cover Changes using Landsat ETM Images at Mendanau North Coastal and Batu Dinding Island in Selat Nasik District, Belitung Regency

2018 ◽  
Vol 12 (1) ◽  
pp. 83-87
Author(s):  
Irma Akhrianti ◽  
Franto Franto ◽  
Eddy Nurtjahya ◽  
Indra Ambalika Syari
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Image Data ◽  
Mining Area ◽  
Land Cover Changes ◽  
Residential Areas ◽  
Mangrove Vegetation ◽  
Open Land ◽  
High Level

Land cover changes is a physical impact which caused by the the existence of human activity that is quite high in parts of the lithosphere of the earth. The change in landscape certainly has a positive correlation with the dynamics of land use in an area, so that regular monitoring needs to be done, because often land use occurs out of control and not even in accordance with its designation. The main problems that occur in Mendanau Isalnd and Batu Dinding Island are the high level of utilization of mangrove ecosystems, the conversion of mangrove land into ecotourism areas, ports area, residental area and mining area (bauxite). This study aims to monitor changes in land cover in the northern coastal areas of Mendanau Island and Batu Dinding Island in Belitung Regency for 6 years (2000, 2002, and 2006) using a remote sensing technology approach, where image data processing refers to a guided classification method combined with check the field. The satellite images used are still classified as low resolution, namely Multitemporal ETM satellite images with ± 10% cloud cover rate. The results showed that, found 6 land cover classes, namely settlement, open land, mangrove vegetation, non-mangrove vegetation, marine waters, and clouds, which can be detected there has been a change in the increase in the area of non-mangrove vegetation by 365.47 ha, while residential areas experienced fluctuating conditions, namely an increase in cover area in 2000-2002 around 111.94 ha, then declined again in 2006 amounting to 61.28 ha. Unlike the case with the area of open land cover and cover of mangrove vegetation which tends to decrease. The area of open land cover in 2000-2002 decreased by 16.96 ha, then declined again in 2006 by 32.32 ha. The cover area of mangrove vegetation in 2000-2002 decreased by 69.5 ha, then decreased again in 2016 amounting to 208.82 ha.  

scholarly journals Hyperspectral Data for Land use/Land cover classification

2014 ◽  
Vol XL-8 ◽  
pp. 991-995 ◽  
Author(s):  
D. Vijayan ◽  
G. Ravi Shankar ◽  
T. Ravi Shankar
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Classification Systems ◽  
Land Use Land Cover ◽  
High Resolution Data ◽  
Liss Iii ◽  
Hyperion Image ◽  
Resolution Data

An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use/ land cover. On-screen interpretation of LISS III (resolution 23.5 m) was compared with Spectral Angle Mapping (SAM) classification of Hyperion (resolution 30m). Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic vegetation and aquatic systems are distinct in Hyperion data. Accuracy assessment of SAM classification of Hyperion compared with the common classification systems followed for LISS III there was no much significant difference between the two. However, more number of vegetation classes could be classified in SAM. There is a misinterpretation of built up and fallow classes in SAM. The advantages of Hyperion over visual interpretation are the differentiation of the type of crop canopy and also crop stage could be confirmed with the spectral signature. The Red edge phenomenon was found for different canopy type of the study area and it clearly differentiated the stage of vegetation, which was verified with high resolution image. Hyperion image for a specific area is on par with high resolution data along with LISS III data.

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