Land cover discrimination at Brazilian Amazon using region based classifier and stochastic distance

Silva, Wagner Barreto da; Pereira, Luciana Oliveira; Sant’Anna, Sidnei J. S.; Freitas, Corina da Costa; Guimarães, Ricardo José de Paula Souza e; Frery, Alejandro C.

doi:10.1109/igarss.2011.6049821

Cited by 7 publications

(13 citation statements)

References 6 publications

(3 reference statements)

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“…However, there is no consensus within the scientific community regarding the best method to integrate optical and SAR data for classification purposes, especially in the areas of tropical rainforest, such as those located in the Brazilian Amazon region. Some authors, including Dutra et al (2002), Lu, Batistella, and Moran (2007), Santos and Messina (2008), Lu et al (2011), Rodrigues and Souza-Filho (2011), and Silva et al (2011, address the fusion using different data from the Amazon region, and they indicate that the information extracted from the integrated data can be advantageous for the LULC classification in the Amazon region. However, there are few studies that are focused on identifying the most suitable method for integrating optical and SAR data in this region.…”

Section: Introductionmentioning

confidence: 98%

“…In recent years, there have been significant advancements in the techniques for fusing synthetic aperture radar (SAR) with optical data, which have improved the classification of the features of interest (Lu, Batistella, and Moran 2007;Dong et al 2009;Huang et al 2010;Silva et al 2011). However, there is no consensus within the scientific community regarding the best method to integrate optical and SAR data for classification purposes, especially in the areas of tropical rainforest, such as those located in the Brazilian Amazon region.…”

Section: Introductionmentioning

confidence: 98%

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Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Pereira

Freitas

Sant´Ánna

et al. 2013

GIScience & Remote Sensing

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This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data. This article also proposes to apply the principal component (PC) technique to the multipolarized synthetic aperture radar (SAR), prior to the optical and radar data PC fusion process, aiming at the use of all available polarized information in the fusion process. Although the fused images improve the visual interpretation of the land use classes, the best results are achieved with the simple combination of the Advanced Land Observing Satellite (ALOS)/phased array L-Band SAR (PALSAR) with the LANDSAT5/Thematic Mapper (TM) images. Radar information is found to be particularly useful for improving the user accuracies (UAs) of Soybean with 40 days after seeding (an increase of about 55%), Dirty Pasture (22%), Degraded Forest and Regeneration (5%), and the producer accuracies (PAs) of Clean Pasture (39%), Fallow Agriculture (16%), Degraded Forest and Regeneration (3%), and Primary Forest (2%). Information from the HH (horizontal transmit and horizontal receive) polarization contributes more than that from HV (horizontal transmit and vertical receive) polarization to discriminate the classes, although the use of both polarizations produces results that are statistically better than those obtained with a single polarization.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Pereira

Freitas

Sant´Ánna

et al. 2013

GIScience & Remote Sensing

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“…An experiment with high resolution remote sensing image shows the superiority of this approach in comparison with the approaches presented in [2] and [4].…”

Section: Introductionmentioning

confidence: 90%

“…The classification process is usually carried out by conventional techniques, such as Maximum Like-Thanks to CAPES, CNPq (Process: 307666/2011-5) and FAPESP (Process: 08/58112-0 and 08/57719-9) agencies for funding. lihood Classification [3], Support Vector Machine [2] or Minimum Distance Classifier based on stochastic distances [4].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine and Bhattacharrya kernel function for region based classification

Negri

Dutra

Sant’Anna

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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Region based methods are indicated to classify image with strong heterogeneity, where only the spectral information is not enough. Different approaches have been proposed to perform this kind of classification. This study presents a new approach for region based classification that consists in use the Support Vector Machine (SVM) method with Bhattacharyya kernel function. A high resolution IKONOS image was classified. The classification results shows that SVM method using the Bhattacharyya kernel is better than Minimum Distance Classifier and conventional SVM.

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“…The most obvious one is that studies with similar study area and objectives can be carried out with different legends because they are based on different sets of data, or they use the same type of data in distinct times or because of differences in field data collection, processing methodologies, or the expected quality of the final mapping. It is the case of the works carried out by [6,11,12,[33][34][35][36][37][38][39][40] within an area in the Lower Tapajós region, Pará state, within the Brazilian Amazon. The resultant maps are rarely directly comparable and their usefulness for studies with other types of data is diminished.…”

Section: Introductionmentioning

confidence: 99%

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

Reis

Escada

Dutra

et al. 2018

Land

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Land Use and Land Cover (LULC) classes defined by subjective criteria can diminish the significance of a study, hindering the reproducibility and the comparison of results with other studies. Having a standard legend for a given study area and objective could benefit a group of researchers focused on long-term or multidisciplinary studies in a given area, in the sense that they would be able to maintain class definition among different works, done by different teams. To allow for reproducibility, it is important that the classes in this legend are described using quantifiable elements of land cover, which can be measured on the ground, as is recommended by Land Cover Meta Language (LCML). The present study aims to propose LCML formalized hierarchical legends for LULC classes, focusing on the southwest of Pará state, within the Brazilian Amazon. In order to illustrate the potential of these legends, a secondary objective of the current study is to analyze classification results using legends derived from a particular Remote Sensing dataset and compare these results with the classification obtained using the LCML hierarchical legend proposed. To perform this analysis, firstly, we proposed a conceptual class model based on existing classification systems for the upland Brazilian Amazon Biome. From this model, 16 LULC classes were described in LCML, using quantifiable and easily recognizable physiognomic characteristics of land cover classes measured on the lower Tapajós river, in Pará state. These classes were grouped into legends with different levels of detail (number of classes), based on our model or on the image and clustering algorithms. All legends were used in supervised classification of a Landsat5/TM image. Results indicate that it is necessary to incorporate multi-temporal knowledge for class definition as well as the proposed thresholds (height and cover proportion of soil, litter, herbaceous vegetation, shrubs, and trees) in order to properly describe classes. However, the thresholds are useful to delimit classes that happen in a successive way. Classification results revealed that classes formed by the same elements of land cover with similar thresholds present high confusion. Additionally, classifications obtained using legends based on the class separability in a given Remote Sensing image tend to be more accurate but not always useful because they can hide or mix important classes. It was observed that the more generalized the legend (those with few details and number of classes), the more accurate the classifications results are for all types of legends.

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Land cover discrimination at Brazilian Amazon using region based classifier and stochastic distance

Cited by 7 publications

References 6 publications

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Support Vector Machine and Bhattacharrya kernel function for region based classification

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

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