Computer Processing of Remotely‐Sensed Images

Mather, Paul M.

doi:10.1002/9780470666517

Cited by 551 publications

(416 citation statements)

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“…After the GLCM is generated for each direction (horizontal, vertical, left diagonal, and right diagonal), the statistical measures are extracted, and then, the four directions are averaged to remove directional effects; this last choice is due to the absence of preferred directions in the geometry of the investigated land-cover classes. Among the several statistical measures which can be extracted from the GLCM to describe specific textural characteristics of the image [28], we chose the following two: the correlation function computed on the IKONOS panchromatic band and the energy function computed on the IKONOS band ratio. The explicit form of the aforementioned functions is…”

Section: Spatial Preprocessingmentioning

confidence: 99%

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Giacco

Thiel

Pugliese

et al. 2010

IEEE Trans. Geosci. Remote Sensing

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Abstract-Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracyassessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and α quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results.

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Section: Spatial Preprocessingmentioning

confidence: 99%

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Giacco

Thiel

Pugliese

et al. 2010

IEEE Trans. Geosci. Remote Sensing

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“…The last or fourth layer is land management developed based on land cover map and land management statistical data of Lesti watershed. Land cover was classified using unsupervised classification method with maximum distance classification (Mather, 1987). The developed four layers were overlaid, and due to the criteria shown in Table 2, the total score was calculated using geo-statistical method.…”

Section: Resultsmentioning

confidence: 99%

The Use of Satellite Remote Sensing Data and Geographic Information Systems on Critical Land Analysis

Suharyanto¹,

Suhartanto²,

Pudyono³

2013

Agrivita.J.Agr.Sci

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Critical land classification can be analyzed using Soil Thickness and BRLKT methods. Both methods need soil erosion data as one of input data. The soil erosion can be analyzed using USLE and MUSLE methods. Base on two critical land analyses methods with input soil erosion data analyzed using two methods will result in four combinations of critical land classification procedures. In this research, critical land classification and soil erosion classification were analyzed using GIS. The best method to classify critical land was investigated in this research, where the best classified critical land is the classified critical land result same with the field condition. Percentage of vegetation cover (PVC) is one of the most important input data in the critical land classification analysis using BRLKT method. This data has 50 of 100 total weights. To analyze this PVC classification, NDVI generated from satellite remote sensing data was employed in this research. From the four results of critical land classified using four procedures used in this research, critical land classified using BRLKT method with input soil erosion analyzed using USLE method produced the critical land classification same with the critical land condition in the field.

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“…Neural Net classifier is also supervised classification using nonparametric approach, which doesn"t use influence of statistic data distributed. Neural classifiers is a machine learning algorithm, which used one model of the brain by building sets of linked processing units such as analogy neural of the brain), then using that algorithm to solve (Mather, 2004). In the same books Matter express the advantages of neural classifiers, i.e.…”

Section: Pixel-based Classificationmentioning

confidence: 99%

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach For the Pattern Consistency of Multi Scale Landcover

juniati

Arrofiqoh

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

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Computer Processing of Remotely‐Sensed Images

Cited by 551 publications

References 0 publications

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

The Use of Satellite Remote Sensing Data and Geographic Information Systems on Critical Land Analysis

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach For the Pattern Consistency of Multi Scale Landcover

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