Anisotropic Rotation Invariant Built-Up Presence Index: Applications to SAR Data

Gamba, Paolo; Pesaresi, Martino; Molch, Katrin; Gerhardinger, Andrea; Lisini, Gianni

doi:10.1109/igarss.2008.4780097

Cited by 17 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PanTex method is based on image texture and therefore is advantageous for multisensor and multitemporal image analysis. The efficiency of this method to delineate built-up areas has been shown to work for optical panchromatic images [26] as well as SAR images [37]. Pesaresi [25,35] has shown that the GLCM contrast statistic is the most efficient at discriminating between built-up and non-built-up areas.…”

Section: Delineating Regions Of Built-up Structures and Urban Growth mentioning

confidence: 99%

Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study

Zeug

Eckert²

2010

Remote Sensing

View full text Add to dashboard Cite

Abstract:In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana'a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where the population doubles almost every ten years. Satellite data from four different sensors were used to explore urban growth in Sana'a between 1989 and 2007, assisted by topographic maps and cadastral vector data. The analysis was conducted by delineating the built-up areas from the various optical satellite data, applying a fuzzy-rule-based composition of anisotropic textural measures and interactive thresholding. The resulting datasets were used to analyse urban growth and changes in built-up density per district, qualitatively as well as quantitatively, using a geographic information system. The built-up area increased by 87 % between 1989 and 2007. Built-up density has increased in all areas, but particularly in the northern and southern suburban districts, also reflecting the natural barrier of surrounding mountain ranges. Based on long-term population figures, geometric population growth was assumed. This hypothesis was used together with census data for 1994 and 2004 to estimate population figures for 1989 and 2007, resulting in overall growth of about 240%. By joining population figures to district boundaries, the spatial patterns of population distribution and growth were examined. Further, urban built-up growth and population changes over time were brought into relation in order to investigate OPEN ACCESSRemote Sensing 2010, 2 1015 changes in population density per built-up area. Population densities increased in all districts, with the greatest density change in the peripheral areas towards the North. The results reflect the pressure on the city's infrastructure and natural resources and could contribute to sustainable urban planning in the city of Sana'a.

show abstract

Section: Delineating Regions Of Built-up Structures and Urban Growth mentioning

confidence: 99%

Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study

Zeug

Eckert²

2010

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Urban Focus Urban Focus is a tool developed by the Remote Sensing Group at the University of Pavia, which implements a slightly generalized version of the PanTeX index [2], with improvements discussed in [3]. It computes -up and is adaptable to panchromatic or SAR data, with different spatial resolutions.…”

Section: Amentioning

confidence: 99%

Towards a free open source software tool for human settlement detection on VHR remote sensing images

Iannelli

Gamba

Dell’Acqua

et al. 2013

Joint Urban Remote Sensing Event 2013

Self Cite

View full text Add to dashboard Cite

This work presents the preliminary results of a joint enterprise aiming at the development of free open source software (FOSS) tools for very high resolution remote sensing (RS) image analysis (IA) at the global level. Specifically, the paper proposes a simple method for the detection of human settlements on RS images. The method relies on a model, which can be described in terms of few simple intuitive features related to the ontology of an urban area. In consequence, the method can be adjusted to different urban typologies by users with little background on RS and IA. Moreover, a FOSS implementation of the proposed method is described. The prototype integrates two free open source tools, namely Urban Focus and InterIMAGE. Both, method and prototype are tested experimentally on a CBERS dataset, whit encouraging results.

show abstract

“…Rotation-invariant anisotropic textural measures have been successfully proposed for the automatic extraction of the information related to the presence of built-up areas using data collected by optical and radar sensors with suitable resolution [1][2] [3]. Based on these textural measures, it is possible to calculate a so-called "built-up presence index" that can be interpreted as a fuzzy membership to the class "built-up".…”

Section: A Rationalmentioning

confidence: 99%

Morphological image filtering for improvement of textural built-up index performances in case of presence of scattered vegetation in semi-desertic areas

Pesaresi

Gerhardinger

2009

2009 Joint Urban Remote Sensing Event

View full text Add to dashboard Cite

in this paper an improved procedure for the automatic recognition of built-up areas, using the so-called PANTEX index is presented. This index is based on analysis of image textural measures extracted using anisotropic rotation-invariant GLCM statistics. These measures may overestimate the built-up areas in case of presence of scattered vegetation having the same spatial pattern of settlements. In this paper we present a methodology able to overcome this problem. This methodology is based on an additional filtering step that pre-selects the image information to be ingested by the textural analysis phase. The test presented here uses multispectral QuickBird satellite data input at the spatial resolution of 2.4 meters. In the selected test area, with the improved procedure we estimated an overall accuracy of 88.69% in the automatic recognition of built-up areas, with an overall increase of accuracy of 20.76% respect to the basic procedure.

show abstract

Anisotropic Rotation Invariant Built-Up Presence Index: Applications to SAR Data

Cited by 17 publications

References 8 publications

Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study

Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study

Towards a free open source software tool for human settlement detection on VHR remote sensing images

Morphological image filtering for improvement of textural built-up index performances in case of presence of scattered vegetation in semi-desertic areas

Contact Info

Product

Resources

About