Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data

Bhaskaran, Sunil; Paramananda, Shanka; Ramnarayan, Maria

doi:10.1016/j.apgeog.2010.01.009

Cited by 194 publications

(103 citation statements)

References 33 publications

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“…First, a segmentation algorithm was operated and a set of objects (segments) was generated, which was semantic meaningful. Many papers used multiresolution segmentation embedded in eCognition software package [27][28][29]. Multiresolution segmentation is also known as Fractal Net Evolution Approach (FNEA), which is based on region growing methods and based on some certain homogeneity criteria: scale parameter, shape and compactness [30,31].…”

Section: Stage 1: Mapping Land-use and Land-cover (Lulc) Using Obiamentioning

confidence: 99%

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

et al. 2016

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Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification.Keywords: land-use and land-cover (LULC); object-based image analysis (OBIA); landscape metrics; support vector machine (SVM); very high resolution (VHR); rural settlement

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Section: Stage 1: Mapping Land-use and Land-cover (Lulc) Using Obiamentioning

confidence: 99%

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

et al. 2016

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“…Remote sensing techniques are important and useful tools for extracting coastal features (Kennedy et al;Maiti and Bhattacharya, 2009;Bhaskaran et al, 2010). In this study, two coastline features detected from Landsat 8 imagery (acquisition date: 27.04.2013) by using object based classification and SVM approach have been compare.…”

Section: Introductionmentioning

confidence: 99%

Comparison of support vector machine and object based classification methods for coastline detection

Kalkan

Bayram

Maktav

et al. 2013

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

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ABSTRACT:Detection of coastline is an important procedure for management of coastal zones. According to the International Geographic Data Committee (IGDC), coastlines are one of the most important environmental heritages on the earth's surface. In the coastal areas, main challenge is to understand the present coastline dynamics and to predict its future developments. Therefore the coastal zone monitoring is an essential process for sustainable coastal management and environmental protection. Shoreline extraction is an important issue for coastal zone monitoring.In this study, efficiency of two different methods for detection of coastline features from satellite images, which cover Lakeland region of Turkey, has been tested. Firstly, object based classification method (OBC) has been used to extract shoreline automatically. Developed process based rule set extracts coastline as a vector file from satellite imagery. As a second method, support vector machine (SVM) algorithm has been used to extract coastline. For the application of these two different methods, Landsat 8 data have been used. The results of these two automatic coastline extraction methods were compared with the results derived from manual digitization process. Random control points over the coastline were used in the evaluation. Results showed that both methods have a sub-pixel accuracy to detect coastline features from Landsat 8 imagery.

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“…Semantic queries in image aspects require integrated visual as well as linguistic analysis that can be accomplished through effective visual translation. Specific approaches are found over the literature where as a generalized integrated attempt is less explored (Graham et al 2005;Bhaskaran et al 2010;Dambach et al 2011).…”

Section: P V Arun Spatial Analysis In Public Health Domain: An Nlpmentioning

confidence: 99%

Spatial Analysis in Public Health Domain: An NLP Approach

Arun

2013

Geodesy and Cartography

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Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.

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Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data

Cited by 194 publications

References 33 publications

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

Comparison of support vector machine and object based classification methods for coastline detection

Spatial Analysis in Public Health Domain: An NLP Approach

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