MAP-MRF Approach to Landsat ETM+ SLC-Off Image Classification

Zhu, Xiaolin; Liu, Desheng

doi:10.1109/tgrs.2013.2247612

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…The impact of the preprocessing on the results need to be evaluated in future work. Besides missing value estimation used in this research, the MRF model can also be used for "data padding" by using spatio-temporal neighborhood information [36]. The MRF models can also be used to "pad" NDVI to cloud covered regions and detect temporal changes in NDVI [37].…”

Section: Discussionmentioning

confidence: 99%

Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Wang

et al. 2015

Remote Sensing

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Seasonal dynamic land cover maps could provide useful information to ecosystem, water-resource and climate modelers. However, they are rarely mapped more frequent than annually. Here, we propose an approach to map dynamic land cover types with frequently available satellite data. Landsat 8 data acquired from nine dates over Beijing within a one-year period were used to map seasonal land cover dynamics. A two-step procedure was performed for training sample collection to get better results. Sample sets were interpreted for each acquisition date of Landsat 8 image. We used the random forest classifier to realize the mapping. Nine sets of experiments were designed to incorporate different input features and use of spatial temporal information into the dynamic land cover classification. Land cover maps obtained with single-date data in the optical spectral region were used as benchmarks. Texture, NDVI and thermal infrared bands were added as new features for improvements. A Markov random field (MRF) model was applied to maintain the spatio-temporal consistency. Classifications with all features from all images were performed, and an MRF model was also applied to the results estimated with all features. The best overall accuracies achieved for each date ranged from 75.31% to 85.61%. OPEN ACCESSRemote Sens. 2015, 7 866

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Section: Discussionmentioning

confidence: 99%

Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Wang

et al. 2015

Remote Sensing

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“…However, despite only have 78 % of data available per scene, it is one of the most radiometrically and geometrically accurate satellite datasets in the world and therefore it is still very useful for various studies (USGS, 2018). For heterogeneous regions, using a Neighbourhood Similar Pixel Interpolator (NSPI) is the simplest and most effective method to interpolate the pixel values within the gaps with high accuracy (Chen et al, 2011;Gao et al, 2016;Liu and Ding, 2017;Zhu et al, 2012;Zhu and Liu, 2014). Therefore, to correct scan line errors, Interactive Data Language (IDL) code for NSPI algorithm developed by Chen et al (2011) was run on ENVI version 5.1.…”

Section: Lulc Mapping and Change Detectionmentioning

confidence: 99%

Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin

Shukla

Ojha

Mijic

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The Upper Ganga River basin is socioeconomically the most important river basin in India and is highly stressed in terms of water resources due to uncontrolled land use and land cover (LULC) activities. This study presents a comprehensive set of analyses to evaluate the population growth, LULC transformations, and water quality nexus for sustainable development in this river basin. The study was conducted at two spatial scales: basin scale and district scale. First, population data were analyzed statistically to study demographic changes, followed by LULC change detection over the period of February–March 2001 to 2012 (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data) using remote sensing and geographical information system (GIS) techniques. Trends and spatiotemporal variations in monthly water quality parameters, viz. biological oxygen demand (BOD), dissolved oxygen (DO, measured in percentage), fluoride (F), hardness (CaCO3), pH, total coliform bacteria and turbidity, were studied using the Mann–Kendall rank test and an overall index of pollution (OIP) developed specifically for this region, respectively. A relationship was deciphered between LULC classes and OIP using multivariate techniques, viz. Pearson's correlation and multiple linear regression. From the results, it was observed that population has increased in the river basin. Therefore, significant and characteristic LULC changes were observed. The river became polluted in both rural and urban areas. In rural areas, pollution is due to agricultural practices, mainly fertilizers, whereas in urban areas it is mainly contributed from domestic and industrial wastes. Water quality degradation has occurred in the river basin, and consequently the health status of the river has also changed from acceptable to slightly polluted in urban areas. Multiple linear regression models developed for the Upper Ganga River basin could successfully predict status of the water quality, i.e., OIP, using LULC classes.

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“…The SLC was completely failed on May 31, 2003. Due to that 22% of pixel information lost in the Landsat-7 data [5]. But remaining 78% of the original (without gaps) data acquired.…”

Section: Introductionmentioning

confidence: 99%

Change detection analysis of reservoirs and lakes in Multi-Temporal Landsat-7 (ETM+) data over the Indian sub-continent during 2008-2018

Naik¹,

Anuradha²,

2018

IJET

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Remote sensing change detection techniques are extensively used in numerous applications such as land cover monitoring, disaster monitoring, and urban sprawl. The main motive of this paper study the change detection analysis of Land Use / Land Cover (LULC) in different lakes and Reservoirs, such as Chilika Lake, Pulicat Lake, Vembanad Lake, Penna Reservoir, and Nagarjuna Sagar Reservoir l ocated in the Indian subcontinent region. The analyses and changes are evaluated during period of 2008 -2018 in multi-temporal Landsat-7 (ETM+) data. The major disadvantage in Landsat-7 for data acquired from satellite sensor, is that it includes strips (gaps) in an image. On May 31, 2003 the Scan-Line-Corrector (SLC) failed completely, due to 22% of pixel information lost in the Landsat-7 data. The focal analysis method is applied to the required image for removing all strips (gaps). Change detection using Image Differencing technique, maximum changed area and unchanged area detect the different Lakes and Reservoirs in the period of 2008-2018. The unsupervised classification is used to compute the accuracy assessment analysis. Excellent results are obtained by using accuracy assessme nt for different Lakes and Reservoirs from 2008 to 2018, with the overall accuracy of 91.59%, and overall kappa statistics of 0.9032. The percentage of a decreased area is more in 2018 as compared to 2008 and it concludes that the percentage of decreased area is more as compared to the percentage of increased area for acquired Landsat-7 data.

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MAP-MRF Approach to Landsat ETM+ SLC-Off Image Classification

Cited by 13 publications

References 32 publications

Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin

Change detection analysis of reservoirs and lakes in Multi-Temporal Landsat-7 (ETM+) data over the Indian sub-continent during 2008-2018

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