Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery

Samaniego, Luis; Schulz, Karsten

doi:10.3390/rs1040875

Cited by 49 publications

(24 citation statements)

References 35 publications

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“…Because of that, Landsat data are widely applied in land cover classification and monitoring on a regional or global scale. Numerous studies have proved the usefulness of Landsat imagery in agricultural land cover classification [7], forest dynamics monitoring [8], urban land use classification [9], other land cover dynamics or land use land cover (LULC) change detection [6,10,11]. Other satellite products such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery have also been widely used for regional scale land cover classification [12][13][14] or land cover change detection [15,16].…”

Section: Introductionmentioning

confidence: 99%

The Improvement of Land Cover Classification by Thermal Remote Sensing

Sun

Schulz

2015

Remote Sensing

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Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE) using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR) bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images.

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Section: Introductionmentioning

confidence: 99%

The Improvement of Land Cover Classification by Thermal Remote Sensing

Sun

Schulz

2015

Remote Sensing

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“…The performance of the automatic classification for marshes (90% of producer's accuracy and 75% of user's accuracy) and water areas (100% of producer's accuracy and 80% of user's accuracy) was highly satisfactory. The performance of the KNN algorithm for our land cover classification approach was enough good and comparable with the accuracy obtained by Franco-Lopez et al (2001) classifying a forest stand (52% of overall accuracy with k=10), and the results of the experiment carried on by Samaniego and Schulz (2009) classifying crop types (47% of overall accuracy with k=5).…”

Section: Land Cover Classificationmentioning

confidence: 62%

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain

Melendez-Pastor¹,

Hernández²,

Pedreño³

et al. 2012

Remote Sensing - Applications

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“…Devese atribuir um valor k, que é o número de vizinhos a serem utilizados na determinação da classe que será atribuída pelos valores de reflectância de superfície da maioria dos pixels circunvizinhos (Meng et al, 2007). Um maior detalhamento do método K-NN, bem como aplicações na área do sensoriamento remoto podem ser encontrados em Meng et al (2007), Samaniego & Schulz (2009) e Vibrans et al (2013. A acurácia dos mapas temáticos gerados foi calculada, a fim de constatar qual classificador gerou os melhores resultados.…”

Section: Methodsunclassified

Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático

Liempt

Messias

Liesenberg

et al. 2015

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o desempenho dos classificadores digitais SVM e K-NN para a classificação orientada a objeto em imagens Landsat-8, aplicados ao mapeamento de uso e cobertura do solo da Alta Bacia do Rio Piracicaba-Jaguari, MG. A etapa de pré-processamento contou com a conversão radiométrica e a minimização dos efeitos atmosféricos. Em seguida, foi feita a fusão das bandas multiespectrais (30 m) com a banda pancromática (15 m). Com base em composições RGB e inspeções de campo, definiramse 15 classes de uso e cobertura do solo. Para a segmentação de bordas, aplicaram-se os limiares 10 e 60 para as configurações de segmentação e união no aplicativo ENVI. A classificação foi feita usando SVM e K-NN. Ambos os classificadores apresentaram elevados valores de índice Kappa (k): 0,92 para SVM e 0,86 para K-NN, significativamente diferentes entre si a 95% de probabilidade. Uma significativa melhoria foi observada para SVM, na classificação correta de diferentes tipologias florestais. A classificação orientada a objetos é amplamente aplicada em imagens de alta resolução espacial; no entanto, os resultados obtidos no presente trabalho mostram a robustez do método também para imagens de média resolução espacial.Termos para indexação: classificação orientada a objetos, gestão territorial, sensoriamento remoto, resolução espacial, uso e cobertura do solo. Comparative analysis of digital classifiers of Landsat-8 images for thematic mapping proceduresAbstract -The objective of this work was to evaluate the performance of SVM and K-NN digital classifiers for the object-based classification on Landsat-8 images, applied to mapping of land use and land cover of Alta Bacia do Rio Piracicaba-Jaguari, in the state of Minas Gerais, Brazil. The pre-processing step consisted of using radiometric conversion and atmospheric correction. Then the multispectral bands (30 m) were merged with the panchromatic band (15 m). Based on RGP compositions and field inspection, 15 land-use and land-cover classes were defined. For edge segmentation, the bounds were set to 10 and 60 for segmentation configuring and merging in the ENVI software. Classification was done using SVM and K-NN. Both classifiers showed high values for the Kappa index (k): 0.92 for SVM and 0.86 for K-NN, significantly different from each other at 95% probability. A major improvement was observed for SVM by the correct classification of different forest types. The object-based classification is largely applied on high-resolution spatial images; however, the results of the present work show the robustness of the method also for medium-resolution spatial images.Index terms: object-based classification, territorial management, remote sensing, spatial resolution, land use and land cover. IntroduçãoA gestão territorial demanda uma constante caracterização dos recursos naturais, além de seu monitoramento contínuo, com o objetivo de sua utilização de forma racional. A execução de projetos de levantamento e mapeamento da superfície terrestre têm-se beneficiado ...

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Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery

Cited by 49 publications

References 35 publications

The Improvement of Land Cover Classification by Thermal Remote Sensing

The Improvement of Land Cover Classification by Thermal Remote Sensing

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain

Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático

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