Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach

Pellizzeri, T.M.; Gamba, Paolo; Lombardo, P.; Dell’Acqua, Fabio

doi:10.1109/tgrs.2003.818762

Cited by 44 publications

(23 citation statements)

References 18 publications

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“…Hence, the discrimination of different classes in urban areas is facilitated. Acqua et al (2003) applied the HDI method using multitemporal imagery and produced better classification results than in the previous study. Additionally, the extraction of streets was also improved by this approach.…”

Section: Introductionmentioning

confidence: 75%

“…Being dissatisfied with the previous result, Acqua et al (2003) modified the methodology with multi-temporal analysis to improve the urban characterization. The main problems of urban area classification by radar imagery are multiple bouncing, layovers, and shadowing.…”

Section: Introductionmentioning

confidence: 95%

“…Despite this improvement, the classifications were still not in satisfactory because of the low separability of some classes, especially of urban area and forest, urban area and industry/greenhouse. Acqua and Gamba (2003) proposed to use the histogram density index (HDI) to identify urban characteristics of high-resolution imagery. However, similar to the finding of Dekker (2003), the approach failed to classify main parts of urban areas like city centers, residential, and suburban areas.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Speckle Divergence and Neighborhood Analysis to Classify Settlement From Terasar-X Data

Komarudin¹,

Indrajit²

2012

IJReSES

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Abstract. The objectives of this research were to develop and improve methods for determination of settlements area with focus on synthetic aperture radar (SAR) data. Remote sensing settlement classification has made great progress, both for optical and radar data as well for their fusion. Yet, in radar imagery, settlement classification still contains some problems. Several studies on application of radar imagery have been conducted using techniques such as textural analysis, multi-temporal analysis, statistical model, spatial indexes, and object-based classification. Most of the development methods have several problems in the specific area especially in the tropical country. Several studies also showed that settlement classification accuracies were just below 60%. This was not sufficient enough to classify settlement areas using SAR imagery. Therefore, in this research, we proposed a new method i.e., the combination of the speckle divergence and the neighborhood analysis. The proposed method was applied to classify settlement area in Cilacap and Padang Districts of Indonesia. The results showed that the proposed method produced a good accuracy i.e., 85.5% for Cilacap Districts and 78.1% for Padang Districts.

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Section: Introductionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Combination of Speckle Divergence and Neighborhood Analysis to Classify Settlement From Terasar-X Data

Komarudin¹,

Indrajit²

2012

IJReSES

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“…Statistical and neural network approaches have been used to classify multi-temporal/multiband SAR sensor images using spatial analaysis 1 . Another approach uses an unsupervised scheme using self-organising map (SOM) neural network to classify the SAR images based on texture classification through graylevel co-occurrence probabilities 2 .…”

Section: T H B R I G H T Threshold For Bright Objectsmentioning

confidence: 99%

Object Hierarchy-based Supervised Characterisation of Synthetic Aperture Radar Sensor Images

Rishabh

Rakshit

2008

DSJ

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A method of supervised characterisation of synthetic aperture radar (SAR) satellite images has been discussed in which simple object shape features of satellite images have been used to classify and describe the terrain types. This scheme is based on a multilevel approach in which objects of interest are first segmented out from the image and subsequently characterised based on their shape features. Once all objects have been characterised, the entire image can be characterised. Emphasis has been laid on the hierarchical information extraction from the image which enables greater flexibility in characterising the image and is not restricted to mere classification. The paper also describes a method for giving relative importance among features, i.e., to give more weights to those features that are better than others in distinguishing between competing classes. A method of comparing two SAR sensor images based on terrain elements present in the images has also been described here.

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“…These three specific topics are examined further in this Special Issue. More generally, spatiotemporal investigation has been used in a wide range of applications, including, for example, soil moisture analysis (Kim and Woodcock 2002, Ferreira et al 2005) and urban growth (Herold et al 2003, Pellizzeri et al 2003, Nichol 2005.…”

Section: Reconciling Space and Timementioning

confidence: 99%

On scales and dynamics in observing the environment

Aplin

2006

International Journal of Remote Sensing

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Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on 'Scales and Dynamics in Observing the Environment'. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of 'Scales and Dynamics in Observing the Environment'.

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Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach

Cited by 44 publications

References 18 publications

Combination of Speckle Divergence and Neighborhood Analysis to Classify Settlement From Terasar-X Data

Combination of Speckle Divergence and Neighborhood Analysis to Classify Settlement From Terasar-X Data

Object Hierarchy-based Supervised Characterisation of Synthetic Aperture Radar Sensor Images

On scales and dynamics in observing the environment

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