A comparative analysis of remote sensing image classification techniques

Sisodia, Pushpendra Singh; Tiwari, Vivekanand; Kumar, Anil

doi:10.1109/icacci.2014.6968245

Cited by 29 publications

(19 citation statements)

References 4 publications

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“…Population_Size=D1.element count 5. Apply optimal solution(D1) 6 Algorithm 1 describes the processing of raw data in which it utilizes a fitness function to drop or select data for processing. The selection of the threshold is done on the basis of the data passed to the optimization function.…”

Section: Existing Work Analysismentioning

confidence: 99%

“…To fulfil these demands, the satellite images aimed at an enhanced spatial resolution at higher frequency range, though, this doesnassure improved land cover. The usage of image classification techniques is considered as a significant factor for improved accuracy [6]. Figure 1 shows a satellite image which contains varios segments like crop, barren etc.…”

Section: Introductionmentioning

confidence: 99%

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A review of remotely sensed satellite image classification

Dhingra

Kumar

2019

IJECE

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<p>Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies.</p>

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Section: Existing Work Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of remotely sensed satellite image classification

Dhingra

Kumar

2019

IJECE

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“…Uma gama de classificadores, aplicados à classificação supervisionada pixel a pixel, tem sido amplamente empregada na classificação de imagens remotamente situadas, e os classificadores Mahalanobis, "maximum likelihood", "minimum distance" e "parellelepiped" têm sido os mais utilizados (Sisodia et al, 2014), todos de natureza paramétrica. Neste contexto, muitos trabalhos têm avaliado a acurácia desses classificadores, para a definição de áreas homogêneas a partir de imagens de resolução espacial moderada (Hagner & Reese, 2007;Prishchepov et al, 2012;Laurin et al, 2013;Jia et al, 2014;Ganasri & Dwarakish, 2015).…”

Section: Introductionunclassified

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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“…With the same fractional abundance images as the input, the different subpixel mapping algorithms obtained different performances and obtained different subpixel mapping results. The reference classification image used to verify the performance of the different subpixel mapping results was obtained by classification of the original high-resolution images by a hard classification method, such as a support vector machine (SVM) [48] or the minimum distance hard classification algorithm [49], which were implemented in ENVI software. For the real hyperspectral image, the low-resolution (LR) hyperspectral image was collected with a Nuance NIR imaging spectrometer, and the high-resolution (HR) image was taken by a digital camera for the same area at the same time.…”

Section: Experiments and Analysismentioning

confidence: 99%

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

Feng

Zhong

Wu³

et al. 2016

Remote Sensing

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Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations.

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A comparative analysis of remote sensing image classification techniques

Cited by 29 publications

References 4 publications

A review of remotely sensed satellite image classification

A review of remotely sensed satellite image classification

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

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

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