Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Lin, Chinsu; Wu, Chao-Cheng; Tsogt, Khongor; Ouyang, Yen-Chieh; Chang, Chein‐I

doi:10.1016/j.inpa.2015.01.003

Cited by 78 publications

(47 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The maximum likelihood method (MLL) classifies a pixel from the spectral response pattern of each category and then assigned to a class (Rujoiu-Mare and Mihai 2016). This method is conducted on individual pixels and the training samples are selected based on visual interpretation which will be further processed to assist in determining signatures of certain class (Lin et al 2015). Pixel-based classification was performed using the same training data which used in object-based classification.…”

Section: Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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...

show abstract

Section: Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The satellite also provides a panchromatic band (PAN, 450-800 nm) with about 0.5 m spatial resolution [26]. A WV2 image of the study area was acquired on May 20, 2011, with a cloud-free and haze-free atmospheric condition for the whole aquaculture area, thus the atmospheric correction was not necessary in the preprocessing step [27]. The MSS image and PAN image were orthorectified into the Universal Transverse Mercator (UTM) projection system, and fused using Gram-Schmidt pan-sharpening method in ENVI (v5.1, Exelis Visual Information Solutions, Boulder, CO, USA, 2014).…”

Section: Data and Preprocessingmentioning

confidence: 99%

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Deng

et al. 2019

Sustainability

View full text Add to dashboard Cite

Coastal aquaculture plays an important role in the provision of seafood, the sustainable development of regional and global economy, and the protection of coastal ecosystems. Inappropriate planning of disordered and intensive coastal aquaculture may cause serious environmental problems and socioeconomic losses. Precise delineation and classification of different kinds of aquaculture areas are vital for coastal management. It is difficult to extract coastal aquaculture areas using the conventional spectrum, shape, or texture information. Here, we proposed an object-based method combining multi-scale segmentation and object-based neighbor features to delineate existing coastal aquaculture areas. We adopted the multi-scale segmentation to generate semantically meaningful image objects for different land cover classes, and then utilized the object-based neighbor features for classification. Our results show that the proposed approach effectively identified different types of coastal aquaculture areas, with 96% overall accuracy. It also performed much better than other conventional methods (e.g., single-scale based classification with conventional features) with higher classification accuracy. Our results also suggest that the multi-scale segmentation and neighbor features can obviously improve the classification performance for the extraction of cage culture areas and raft culture areas, respectively. Our developed approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.Environmental Protection Law, overall marine functional zonation, and nature reserve schemes, have been formulated for the management of coastal areas. However, comprehensive coastal management in China is still a big challenge. Thus, the monitoring and management of coastal aquaculture are imperative to ensure sustainable development of marine aquaculture industry.Remote sensing technology has substantially improved our ability to observe remote or inaccessible areas at a fraction of the cost of traditional surveys [9]. Mapping and monitoring of aquaculture facilities provide decision-makers with important baseline data on production, cultivated area boundaries, and environmental impacts [10,11]. Remote sensing can provide consistent and wide-range monitoring using various sensors to support aquaculture management [12][13][14], which means the mapping of aquaculture facilities can be performed accurately and periodically at selected scales.Previous studies have used visual interpretation, spatial structure enhanced analysis, and object based image analysis (OBIA) to extract coastal aquaculture areas from remotely sensed imagery. Although visual interpretation can achieve the highest accuracy, it takes a lot of time and effort. Thus, it is less used presently. Spatial structure enhancement techniques, such as neighborhood and texture analysis, are the most commonly adopted methods in the classification process [15,16]. OBIA has become a widely used method in the past decades, especially for the classification o...

show abstract

“…For this research, a nonlinear radial basis function kernel of gamma (γ) = 0.01 was used for the Pleiades-1A image in 2013 based on previous literature and testing. For example, Huang et al [11] tested values of γ from 0.1 to 1 and found γ = 0.1 gave the optimum boundaries among classes with less misclassification rate, whereas γ = 0.01 was used by Lin et al [15] in classifying 10 LULC features. However, a γ of 0.05 was selected for the 2014 Pleiades-1A and 2016 SPOT-6, as our testing found it to have the best result for these images.…”

Section: Image Fusionmentioning

confidence: 99%

“…Recent examples in the literature show that SVMs used with both pixels [13] and image objects [14,15] can outperform other classification algorithms (e.g., Decision Tree, Maximum Likelihood, Nearest Neighbor and Neural Networks). The SVM classifier is not based directly on differences between the statistical distribution of attributes of separate classes [15,16] but instead it uses non-parametric machine learning algorithms that determine the optimal boundaries among classes [11]. The SVM classifier is noteworthy for its ability to separate complex classes with limited training data [17].…”

Section: Introductionmentioning

confidence: 99%

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

et al. 2017

View full text Add to dashboard Cite

Abstract:The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images-Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m 2 -or 0.2% of the total catchment area-in March 2013 to 121,000 m 2 -or 0.3%-in May 2014, and then a decrease to 39,000 m 2 -or 0.1%-in January 2016.

show abstract

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Cited by 78 publications

References 54 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

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

Contact Info

Product

Resources

About