scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

Author(s):  
D. Amarsaikhan

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

Author(s):  
H. J. Hernández ◽  
M. A. Gutiérrez ◽  
M. P. Acuña

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (<i>end members</i>): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All <i>end members</i> were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.


Author(s):  
H. J. Hernández ◽  
M. A. Gutiérrez ◽  
M. P. Acuña

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (<i>end members</i>): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All <i>end members</i> were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.


Author(s):  
Trinh Le Hung

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.


2018 ◽  
Vol 7 (12) ◽  
pp. 453 ◽  
Author(s):  
Mst Ilme Faridatul ◽  
Bo Wu

Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94-96%, which is superior to the accuracy of the SVM algorithm.


2019 ◽  
Vol 11 (18) ◽  
pp. 2128 ◽  
Author(s):  
Mugiraneza ◽  
Nascetti ◽  
Ban

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.


2021 ◽  
Vol 62 (1) ◽  
pp. 1-9
Author(s):  
Hung Le Trinh ◽  
Ha Thu Thi Le ◽  
Loc Duc Le ◽  
Long Thanh Nguyen ◽  

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.


2016 ◽  
Vol 44 (6) ◽  
pp. 855-863 ◽  
Author(s):  
Masoud Habibi ◽  
Mahmod Reza Sahebi ◽  
Yasser Maghsoudi ◽  
Shaheen Ghayourmanesh

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