Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images

In this article, the detection of urban areas on satellite multispectral Landsat images. The goal is to improve the visual interpretations of images from remote sensing experts who often remain subjective. Interpretations depend deeply on the quality of segmentation which itself depends on the quality of samples. A remote sensing expert must actually prepare these samples. To enhance the segmentation process, this article proposes to use genetic algorithms to evolve the initial population of samples picked manually and get the most optimal samples. These samples will be used to train the Kohonen maps for further classification of a multispectral satellite image. Results are obtained by injecting genetic algorithms in sampling phase and this paper proves the effectiveness of the proposed approach.

Download Full-text

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133321991814 ◽

2021 ◽

pp. 030913332199181

Author(s):

Andreas Christian Braun

Keyword(s):

Remote Sensing ◽

Land Use ◽

Geographic Information Systems ◽

Image Classification ◽

Satellite Image ◽

Physical Geography ◽

Data Availability ◽

Scientific Results ◽

Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

Download Full-text

Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

Forum Geografi ◽

10.23917/forgeo.v19i1.4567 ◽

2017 ◽

Vol 19 (1) ◽

pp. 1

Author(s):

Beny Harjadi

Keyword(s):

Remote Sensing ◽

Field Survey ◽

Critical Level ◽

Satellite Image ◽

Geographic Information ◽

Level Data ◽

The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

Download Full-text

Optical Remote Sensing

Urban Informatics - The Urban Book Series ◽

10.1007/978-981-15-8983-6_20 ◽

2021 ◽

pp. 315-344

Author(s):

Man Sing Wong ◽

Xiaolin Zhu ◽

Sawaid Abbas ◽

Coco Yin Tung Kwok ◽

Meilian Wang

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Information Extraction ◽

Urban Areas ◽

Satellite Image ◽

Background Information ◽

Optical Remote Sensing ◽

Basic Operation ◽

City Development ◽

Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

Download Full-text

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

Geospatial Intelligence ◽

10.4018/978-1-5225-8054-6.ch072 ◽

2019 ◽

pp. 1624-1644

Author(s):

Gabriele Nolè ◽

Rosa Lasaponara ◽

Antonio Lanorte ◽

Beniamino Murgante

Keyword(s):

Remote Sensing ◽

Spatial Autocorrelation ◽

Urban Areas ◽

Urban Expansion ◽

Data Sets ◽

Temporal Data ◽

Landsat Images ◽

Urban Settlements ◽

Multi Temporal ◽

Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Download Full-text

A Study on Supervised Classification of Remote Sensing Satellite Image by Bayesian Algorithm Using Average Fuzzy Intracluster Distance

Lecture Notes in Computer Science - Combinatorial Image Analysis ◽

10.1007/978-3-540-30503-3_44 ◽

2004 ◽

pp. 597-606 ◽

Cited By ~ 2

Author(s):

Young-Joon Jeon ◽

Jae-Gark Choi ◽

Jin-Il Kim

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Satellite Image ◽

Bayesian Algorithm ◽

Remote Sensing Satellite

Download Full-text

Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns

Urban Science ◽

10.3390/urbansci3040101 ◽

2019 ◽

Vol 3 (4) ◽

pp. 101 ◽

Cited By ~ 4

Author(s):

Lucille Alonso ◽

Florent Renard

Keyword(s):

Remote Sensing ◽

Air Temperature ◽

Urban Areas ◽

Land Development ◽

Explanatory Variables ◽

Dense Networks ◽

Urban Heat ◽

Derived Data ◽

Computer Resources

With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.

Download Full-text