Performance of LAPAN-A2 satellite data to classify land cover/land use in Semarang, Central Java

Nugroho, Jalu Tejo; Zylshal, Zylshal; Chulafak, Galdita Aruba; Kushardono, Dony

doi:10.1088/1755-1315/54/1/012098

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Overall performance of objectbased classification on LAPAN-A2 on extracting LC/LU information was lower than pixel-based classification or visual interpretation previously done by Nugroho et al (2017) with 61.77% accuracy. This result indicates that interpretability of LAPAN-A2 data is around 62%.…”

Section: Resultscontrasting

confidence: 58%

“…Figure 3-2 and 3-3 further show that a relatively homogenous water body (indicates by green rectangle on Figure 3-3) tends to have a high variation of digital number on LAPAN-A2 data compared with Pleiades-1A data. Performing smoothing algorithm prior to segmentation and classification process might be able to reduce this effect.Overall performance of objectbased classification on LAPAN-A2 on extracting LC/LU information was lower than pixel-based classification or visual interpretation previously done by Nugroho et al (2017) with 61.77% accuracy. This result indicates that interpretability of LAPAN-A2 data is around 62%.…”

contrasting

confidence: 58%

“…The study area extends between longitudes 110⁰21'9.31'' E -110⁰24'3.22'' E and latitudes 6⁰56'3.5'' S -6⁰58'57.72'' S ( Figure 2-1). The data has been geometric corrected using 25 control points extracted with total RMSE about 5.62 (Nugroho et al 2017). …”

Section: Location and Datamentioning

confidence: 99%

“…Previous study has shown that the overall accuracy of LAPAN-A2 for LU/LC classification in Semarang by visual interpretation is about 61.77%. Camera quality, off-nadir view and weather condition are some factors which may affect in accuracy (Nugroho et al 2017). It is considered necessary to assess the LU/LC classification by using digital classification since visual interpretation is subjective and requires experienced interpreter while digital classification also offers faster results (Zylshal et al 2016).…”

mentioning

confidence: 99%

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A Comparison of Object-Based and Pixel-Based Approaches for Land Use/Land Cover Classification Using Lapan-A2 Microsatellite Data

Nugroho

Zylshal

Sari

et al. 2017

IJReSES

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Abstract. In recent years, small satellite industry has been a rapid trend and become important especially when associated with operational cost, technology adaptation and the missions. One mission of LAPAN-A2, the 2 nd generation of microsatellite that developed by Indonesian National Institute of Aeronautics and Space (LAPAN), is Earth observation using digital camera that provides imagery with 3.5 m spatial resolution. The aim of this research is to compare between object-based and pixel-based classification of land use/land cover (LU/LC) in order to determine the appropriate classification method in LAPAN-A2 data processing (case study Semarang, Central Java).The LU/LC were classified into eleven classes, as follows: sea, river, fish pond, tree, grass, road, building 1, building 2, building 3, building 4 and rice field. The accuracy of classification outputs were assessed using confusion matrix. The object-based and pixel-based classification methods result for overall accuracy are 31.63% and 61.61%, respectively. According to accuracy result, it was thought that blurring effect on LAPAN-A2 data may be the main cause of accuracy decrease. Furthermore, the result is suggested to use pixel-based classification to be applied in LAPAN-A2 data processing. Keywords: LAPAN-A2 microsatellite, LU/LC, object-based, pixel-based INTRODUCTIONThe utilization of small satellite for remote sensing is increasing in recent years. Over the past 50 years, more than 1500 small satellites have been launched worldwide with well-focus on Earth observation missions. More than half of them are classified as micro satellites with the mass between 10 and 100 kg (Sandau and Brieb 2008;Gupta et al. 2016). The term "faster, cheaper and smaller" that addressed to this satellite, generally explains that the microsatellite technology is developed by countries who want to start with effective costs and affordable technology (Vincent et al. 1998;Gardner et al. 1996).The diverse Earth monitoring based on microsatellite data has been done, included: a) hotspot detection, fires and volcanic eruptions (Walter et al. 2005), b) environment monitoring, such as land use classification (Qian 2008), land surface and vegetation analysis (Becker et al. 1996), agriculture, hydrology, urban and coastal area (Laguarde et al. 2010), water quality (Matjafri et al. 2002), global 3D imaging (Yang andYang 2002) and c) disaster monitoring, such as cyclone, flood, drought, landslide, pollution, (Yong et al. 2008;Sandau and Brieb 2008), impending earthquake forecast (Qiang et al. 2000), etc.Microsatellite development is also becoming a concern of Indonesian National Institute of Aeronautics and Space (LAPAN). The mission of LAPAN-A2, as first equatorial microsatellite developed International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 1 June 2017: 27 -36 by LAPAN, are maritime monitoring, disaster mitigation supporting and Earth observing by using RGB matrix camera (Hardhienata et al. 2011). This microsatellite has altitude 650 km the digital space...

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Section: Resultscontrasting

confidence: 58%

contrasting

confidence: 58%

Section: Location and Datamentioning

confidence: 99%

mentioning

confidence: 99%

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A Comparison of Object-Based and Pixel-Based Approaches for Land Use/Land Cover Classification Using Lapan-A2 Microsatellite Data

Nugroho

Zylshal

Sari

et al. 2017

IJReSES

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“…The LAPAN-A3 data is expected to be complementary to Landsat-8 data as well as medium resolution data from other operational satellites. A previous study of LAPAN-A2 shows that the accuracy obtained in the identification of urban land cover (case study of Semarang, Central Java) when the satellite is in offnadir position reaches more than 60%, both using visual interpretation (Nugroho et al 2017a) or digital classification (Nugroho et al 2017b).…”

Section: Introductionmentioning

confidence: 97%

Lapan-A3 Satellite Data Analysis for Land Cover Classification (Case Study: Toba Lake Area, North Sumatra)

Nugroho¹,

Zylshal²,

Kushardono³

2018

IJReSES

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LAPAN-A3 is the 3rdgeneration satellite for remote sensing developed by National Institute of Aeronautics and Space (LAPAN). The camera provides imagery with 15 m spatial resolution and able to view a swath 120 km wide. This research analyzes the performance of LAPAN-A3 satellite data to classify land cover in Toba Lake area, North Sumatera. Data processing starts from the selection of region of interest up to the assessment of accuracy. Supervised classification with maximum likelihood approach and confusion matrix method was applied to classify and evaluate the assessment results. The land cover is classified into five classes; water, bare land, agriculture, forest and secondary forest. The result of accuracy test is 93.71%. It proves that LAPAN-A3 data could classify the land cover accurately. The data is expected to complement the need of the satellite data with medium spatial resolution.

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An Adaptive Stripe Noise Removal Algorithm for Optical Satellite Imagery

Kamirul

Hakim

Hasbi

2019

2019 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

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Performance of LAPAN-A2 satellite data to classify land cover/land use in Semarang, Central Java

Cited by 5 publications

References 13 publications

A Comparison of Object-Based and Pixel-Based Approaches for Land Use/Land Cover Classification Using Lapan-A2 Microsatellite Data

A Comparison of Object-Based and Pixel-Based Approaches for Land Use/Land Cover Classification Using Lapan-A2 Microsatellite Data

Lapan-A3 Satellite Data Analysis for Land Cover Classification (Case Study: Toba Lake Area, North Sumatra)

An Adaptive Stripe Noise Removal Algorithm for Optical Satellite Imagery

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