Image Enhancement and Feature Extraction Based on Low-Resolution Satellite Data

Syrris, Vasileios; Stefano, Ferri; Ehrlich, Daniele; Pesaresi, Martino

doi:10.1109/jstars.2015.2417864

Cited by 28 publications

(14 citation statements)

References 13 publications

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“…PanTex (window size 105) was the most important feature in the set. This confirms the findings of [42]. However, PanTex strongly depends on the contrast level, thus contrast enhancement is important to distinguish slums.…”

Section: Quantitative Analysissupporting

confidence: 88%

Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia

et al. 2018

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The survey-based slum mapping (SBSM) program conducted by the Indonesian government to reach the national target of “cities without slums” by 2019 shows mapping inconsistencies due to several reasons, e.g., the dependency on the surveyor’s experiences and the complexity of the slum indicators set. By relying on such inconsistent maps, it will be difficult to monitor the national slum upgrading program’s progress. Remote sensing imagery combined with machine learning algorithms could support the reduction of these inconsistencies. This study evaluates the performance of two machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), for slum mapping in support of the slum mapping campaign in Bandung, Indonesia. Recognizing the complexity in differentiating slum and formal areas in Indonesia, the study used a combination of spectral, contextual, and morphological features. In addition, sequential feature selection (SFS) combined with the Hilbert–Schmidt independence criterion (HSIC) was used to select significant features for classifying slums. Overall, the highest accuracy (88.5%) was achieved by the SVM with SFS using contextual, morphological, and spectral features, which is higher than the estimated accuracy of the SBSM. To evaluate the potential of machine learning-based slum mapping (MLBSM) in support of slum upgrading programs, interviews were conducted with several local and national stakeholders. Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale.

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Section: Quantitative Analysissupporting

confidence: 88%

Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia

et al. 2018

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“…(2) UV-building: Every UV patch in the city-UV structure can be viewed as the landscape in the UV-building structure. To create building patches for the UV landscape, we employ the morphological building index (MBI) [44][45][46] for unsupervised and rapid extraction from the high-resolution images, which has been used in a number of recent studies including built-up area detection [47] and urban change detection [48]. We also create vegetation patches using the normalized difference vegetation index (NDVI).…”

Section: City-uv-building Hierarchical Landscape Modelmentioning

confidence: 99%

The Use of Landscape Metrics and Transfer Learning to Explore Urban Villages in China

et al. 2017

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Urban villages (UVs), the main settlements of rural migrant workers and low-income groups in metropolitan areas of China, have become of major concern to city managers and researchers due to the rapid urbanization in recent years. A clear understanding of their evolution and spatial relationships with the city is of great importance to policy formulation, implementation and assessment. In this paper, we propose a new framework based on landscape metrics and transfer learning for the long-term monitoring and analysis of UVs, and we apply it to Shenzhen and Wuhan, two metropolitan cities of China, with high-resolution satellite images acquired from 2003-2012 and 2009-2015, respectively. In the framework, landscape metrics are used for identifying the UVs and quantifying their evolution patterns on the basis of a city-UV-building hierarchical landscape model. Transfer learning is also introduced to use the samples and features across the spatial and temporal domains, which reduces the time and labor cost, as well as improves the mapping accuracies by 3-10%. The results show that the total area of UVs has decreased by less than 6% in Shenzhen and more than 45% in Wuhan. Moreover, we observe significant spatial correlations in the development of UVs in Shenzhen. By contrast, no strong spatial correlations are found in Wuhan's UVs, indicating that their development is largely independent of the spatial location. The results reveal two typical strategies, i.e., demolition and renovation, towards the redevelopment of UVs in China.

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“…[18] presents a local texture based on graph model in a pointwise approach, and [19] introduces a spatial feature based on mean shift vector and extends the feature for object-based classification. Another paper [20] evaluates the sensitivity of spatial features (PANTEX and MBI [21]) against contrast adjustment for built-up detection.…”

Section: Feature Extractionmentioning

confidence: 99%

Foreword to the Special Issue on Information Extraction From High-Spatial-Resolution Optical Remotely Sensed Imagery

Huang

Fauvel

Mura

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Image Enhancement and Feature Extraction Based on Low-Resolution Satellite Data

Cited by 28 publications

References 13 publications

Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia

Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia

The Use of Landscape Metrics and Transfer Learning to Explore Urban Villages in China

Foreword to the Special Issue on Information Extraction From High-Spatial-Resolution Optical Remotely Sensed Imagery

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