scholarly journals Classification of Multi-temporal Images using Machine Learning

2021 ◽  
Author(s):  
Simranjit Singh Pabla ◽  
Mandeep Singh Mandla ◽  
Hardik Narendra ◽  
Swasti Patel
Keyword(s):  
Supervised Classification ◽  
Kappa Coefficient ◽  
Support Vector ◽  
New Approach ◽  
Object Based Image Analysis ◽  
Novel Approach ◽  
Object Based ◽  
Multi Temporal ◽  
Over Time

In past, there has been a lot of research related to the image-based technique in remote sensing from which object-based classification is giving great results among all the techniques. This paper presents a new approach where we have mixed both OBIA (Object-Based Image Analysis) & supervised classification. And with this novel approach, our team aims to do classification as well as analysis for the change detection over time. The data used in this study is high-resolution Multispectral 4-band images from 2017 to 2019 (i.e. 3.0 m) provided by the PlanetScope satellite of region Chandigarh, India. Here the data has been pre-processed through passing it in a pipeline of steps and used a Multi-resolution segmentation algorithm and classify the 7 classes through supervised learning using 3 algorithms Maximum Likelihood (ML), Support Vector Machine (SVM), Mahalanobis Distance (MD). And out of the three, SVM and ML has given the highest Overall Accuracy of 95.21% & Kappa Coefficient = 0.9159 and Overall Accuracy 91.91% & Kappa Coefficient = 0.8860. Altogether; this is a highly effective approach for classification and detecting the change in Urban area or Rural area or forest area than simply using OBIA or pixel-based approach.

scholarly journals Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

2015 ◽  
Vol 3 (9) ◽  
pp. 5633-5664 ◽  
Author(s):  
S. Heleno ◽  
M. Matias ◽  
P. Pina ◽  
A. J. Sousa
Keyword(s):  
Support Vector ◽  
Multispectral Images ◽  
Madeira Island ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Digital Elevation ◽  
Landslide Event ◽  
Separate Source ◽  
Elevation Model

Abstract. A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

scholarly journals A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach

2021 ◽  
Vol 13 (12) ◽  
pp. 2317
Author(s):  
Gerard Summers ◽  
Aaron Lim ◽  
Andrew J. Wheeler
Keyword(s):  
Image Analysis ◽  
Spatial Resolution ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Seabed Sediment ◽  
Object Based Image Analysis ◽  
Bathymetric Data ◽  
Sediment Waves ◽  
Object Based

National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas.

SiRCub, A Novel Approach to Recognize Agricultural Crops Using Supervised Classification

2017 ◽  
Vol 8 (4) ◽  
pp. 20-36
Author(s):  
Jordi Creus Tomàs ◽  
Fabio Augusto Faria ◽  
Júlio César Dalla Mora Esquerdo ◽  
Alexandre Camargo Coutinho ◽  
Claudia Bauzer Medeiros
Keyword(s):  
Time Series ◽  
Supervised Classification ◽  
Agricultural Research ◽  
Support Vector ◽  
Agricultural Crop ◽  
Mato Grosso ◽  
New Approach ◽  
Novel Approach ◽  
Research Corporation ◽  
Ndvi Time Series

This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.

scholarly journals Comparison of GEOBIA classification algorithms based on Worldview-3 imagery in the extraction of coastal coniferous forest

2021 ◽  
Vol 145 (11-12) ◽  
pp. 535-544
Author(s):  
Lovre Panđa ◽  
Rina Milošević ◽  
Silvija Šiljeg ◽  
Fran Domazetović ◽  
Ivan Marić ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
Coniferous Forest ◽  
Kappa Coefficient ◽  
Random Trees ◽  
Support Vector ◽  
Classification Algorithms ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

Šume primorskih četinjača, sa svojom ekološkom, ekonomskom, estetskom i društvenom funkcijom, predstavljaju važan dio europskih šumskih zajednica. Osnovni cilj ovoga rada je usporediti najkorištenije GEOBIA (engl. Geographic Object-Based Image Analysis) klasifikacijske algoritme (engl. Random Trees – RT, Maximum Likelihood – ML, Support Vector Machine – SVM) s ciljem izdvajanja šuma primorskih četinjača na visoko-rezolucijskom WorldView-3 snimku unutar topografskog slijevnog područja naselja Split. Metodološki okvir istraživanja uključuje (1) izvođenje izoštrenog multispektralnog snimka (WV-3<sub>MS</sub>-a); (2) testiranje segmentacijskih korisničko-definiranih parametara; (3) dodavanje testnih uzoraka; (4) klasifikaciju segmentiranog modela; (5) procjenu točnosti klasifikacijskih algoritama, te (6) procjenu točnosti završnog modela. RT se prema korištenim pokazateljima (correctness – COR, completeness – COM i overall quality – OQ) pokazao kao najbolji algoritam. Iterativno postavljanje segmentacijskih parametara omogućilo je detekciju najprikladnijih vrijednosti za generiranje segmentacijskog modela. Utvrđeno je da sjene mogu uzrokovati značajne probleme ako se klasificiranje vrši na visoko-rezolucijskim snimkama. Modificiranim Cohen’s kappa coefficient (K) pokazateljem izračunata je točnost konačnog modela od 87,38%. WV-3<sub>MS</sub> se može smatrati kvalitetnim podatkom za detekciju šuma primorskih četinjača primjenom GEOBIA metode.

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.

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

&lt;p&gt;Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy. &amp;#160;As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.&lt;/p&gt;

PEMBUATAN PETA TUTUPAN LAHAN BERDASARKAN HASIL KLASIFIKASI DENGAN METODE OBJECT BASED IMAGE ANALYSIS (OBIA) PADA FOTO UDARA (Studi Kasus di Desa Wates, Kecamatan Wates, Kabupaten Kulon Progo)

2021 ◽  
pp. 355
Author(s):  
Galuh Qori’ah Fahmah Suratno ◽  
Anindya Sricandra Prasidya
Keyword(s):  
Image Analysis ◽  
Kappa Coefficient ◽  
Spectral Difference ◽  
Object Based Image Analysis ◽  
Object Based

Manusia berperan penting dalam adanya alih fungsi lahan. Dengan adanya alih fungsi lahan dibutuhkan media yang bisa menampilkan tutupan lahan suatu daerah, yaitu peta tutupan lahan. Peta tutupan lahan dibuat dari data ortofoto dengan metode klasifikasi Object Based Image Analysis (OBIA). Metode segmentasi yang digunakan adalah multiresolution dan spectral difference. Kelas tutupan lahan yang mendominasi Desa Wates adalah vegetasi sebesar 31,49% dengan luas 1.296.311,80 m2, pemukiman sebesar 30,03% dengan luas 1.236.325,31 m2, sawah sebesar 25,16% dengan luas 1.035.923,43 m2, jalan sebesar 6,19% dengan luas 254.774,38 m2, lahan terbuka sebesar 3,55% dengan luas 146.090,24 m2, sungai sebesar 2% dengan luas 82.387,70 m2, dan rel kereta api sebesar 1,59% dengan luas 65.331,42 m2. Dari hasil perhitungan, didapatkan nilai overall accuracy sebesar sebesar 74,2857% dan nilai kappa coefficient sebesar 0,7. Nilai tersebut tidak memenuhi syarat batas nilai minimum sebesar 85% karena perbedaan spasial dan temporal media uji yang tidak sebanding.

scholarly journals Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

2014 ◽  
Vol XL-2/W3 ◽  
pp. 191-196 ◽  
Author(s):  
S. Mirzaee ◽  
M. Motagh ◽  
H. Arefi ◽  
M. Nooryazdan
Keyword(s):  
Environmental Changes ◽  
Support Vector ◽  
Svm Classifier ◽  
Sensing Applications ◽  
Imaging Characteristics ◽  
Training Samples ◽  
Study Results ◽  
Phenological Changes ◽  
Multi Temporal

Due to its special imaging characteristics, Synthetic Aperture Radar (SAR) has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM) classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

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