scholarly journals Object-based classification of vegetation species in a subtropical wetland using Sentinel-1 and Sentinel-2A images

2021 ◽  
Vol 3 ◽  
pp. 100017
Author(s):  
Luis Fernando Chimelo Ruiz ◽  
Laurindo Antonio Guasselli ◽  
João Paulo Delapasse Simioni ◽  
Tássia Fraga Belloli ◽  
Pâmela Caroline Barros Fernandes
Keyword(s):  
Subtropical Wetland ◽  
Object Based ◽  
Sentinel 2A ◽  
Vegetation Species

scholarly journals Performance of Sentinel-2A Remote Sensing System for Urban Area Mapping in Malaysia via Pixel-Based and OBIA Methods

2021 ◽  
Vol 18 (21) ◽  
pp. 38
Author(s):  
Adhwa Amir Tan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Nur Shafira Nisa Shaharum
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Spatial Resolution ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based ◽  
Fusion Images ◽  
Urban Mapping ◽  
Sentinel 2A

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. HIGHLIGHTS An accurate mapping of urban land is still challenging and requires high image quality of spectral and spatial aspects to identify features Single and fusion image analysis conducted in order to investigate and assess the most performing interpretation result by grouping out the features classes Statistical performance and image classification comparison is relevant to prove the most effective result among the images GRAPHICAL ABSTRACT

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

<p>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.  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.</p>

scholarly journals Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

2018 ◽  
Vol 10 (11) ◽  
pp. 1751 ◽  
Author(s):  
Abderrahim Nemmaoui ◽  
Manuel A. Aguilar ◽  
Fernando J. Aguilar ◽  
Antonio Novelli ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Free Access ◽  
Landsat 8 ◽  
Horticultural Crops ◽  
Command Line Tool ◽  
Multi Temporal ◽  
Tree Models ◽  
Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

scholarly journals Identifikasi Gosong Karang Mengggunakan Citra Satelit Sentinel 2A (Studi Kasus: Perairan Pesisir Nias Utara)

2020 ◽  
Vol 21 (1) ◽  
pp. 95-108
Author(s):  
Anang Dwi Purwanto ◽  
Teguh Prayogo ◽  
Sartono Marpaung
Keyword(s):  
Coral Reefs ◽  
Image Classification ◽  
Classification Method ◽  
Fishing Ground ◽  
Field Observations ◽  
Geometric Correction ◽  
Radiometric Correction ◽  
Object Based ◽  
Sentinel 2A ◽  
North Sumatra

ABSTRACTThe waters of Northern Nias, North Sumatra Province have a great potential for natural resources, one of which is the reef which is often used as a fishing ground. This study aims to identify and monitor the distribution of coral reefs around the waters of Northern Nias. The location of study is limited by coordinates 97° 0'31'' - 97° 16'54'' E and 1° 29'2'' LU - 1° 6'24'' N. The study locations were grouped in 6 (six) areas including Mardika reef, Wunga reef, Mausi1 reef, Mausi2 reef, Tureloto reef and Senau reef. The data used were Sentinel 2A imagery acquisition on 19 September 2018 and field observations made on 6-12 September 2018. Data processing includes geometric correction, radiometric correction, water column correction and classification using pixel-based and object-based methods as well as by delineating on the image. One classification method will be chosen that is most suitable for the location of the reef. The results show Sentinel 2A was very helpful in mapping the distribution of coral reefs compared to direct observation in the field. The use of image classification method rightly is very helpful in distinguishing coral reef objects from surrounding objects. The estimated area of coral reefs was 1,793.20 ha with details of the Mardika reef 143.27 ha, Wunga reef 627.06 ha, Mausi1 reef 299.84 ha, Mausi2 reef 141.873 ha, Tureloto reef 244.73 ha, Senau reef 336.44 ha. The existence of coral reefs have a high potential as a fishing ground and a natural tourist attraction.Keywords: coral reefs, sentinel 2A, lyzenga 1978, image classification, Northern NiasABSTRAKPerairan Nias Utara yang terletak di Provinsi Sumatra Utara memiliki potensi kekayaan alam yang besar dimana salah satunya adalah gosong karang yang sering dijadikan lokasi penangkapan ikan oleh nelayan. Penelitian ini bertujuan untuk mengidentifikasi dan monitoring sebaran gosong karang di sekitar perairan Nias Utara. Lokasi penelitian dibatasi dengan koordinat 97°0’31’’ - 97°16’54’’ BT dan 1°29’2’’LU – 1°6’24’’  LU. Untuk mempermudah dalam pengolahan data maka lokasi kajian dikelompokkan dalam 6 (enam) kawasan diantaranya gosong Mardika, gosong Wunga, gosong Mausi1, gosong Mausi2, gosong Tureloto dan gosong Senau. Data yang digunakan adalah citra satelit Sentinel 2A hasil perekaman tanggal 19 September 2018 dan hasil pengamatan lapangan yang telah dilakukan pada tanggal 6 - 12 September 2018. Pengolahan data meliputi koreksi geometrik, koreksi radiometrik, koreksi kolom air dan klasifikasi menggunakan metode klasifikasi berbasis piksel dan berbasis objek serta deliniasi citra. Dari ketiga metode klasifikasi tersebut akan dipilih satu metode klasifikasi yang sesuai dengan lokasi gosong karang. Hasil penelitian menunjukkan citra Sentinel 2A sangat membantu dalam memetakan sebaran gosong karang dibandingkan dengan pengamatan langsung di lapangan. Pemilihan metode klasifikasi citra satelit yang tepat sangat membantu dalam membedakan objek gosong karang dengan objek di sekitarnya. Estimasi total luasan gosong karang di perairan Nias Utara adalah 1,793.20 ha dengan rincian luasan gosong karang Mardika 143.27 ha, gosong Wunga 627.06 ha, gosong Mausi1 299.84 ha, gosong Mausi2 141.873 ha, gosong Tureloto 244.73 ha, gosong Senau 336.44 ha. Keberadaan gosong karang memiliki potensi yang tinggi sebagai lokasi penangkapan ikan dan memiliki daya tarik sebagai tempat wisata alam.Kata kunci: gosong karang, sentinel 2A, lyzenga 1978, klasifikasi citra, Nias Utara

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