Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression

Rosentreter, Johannes; Hagensieker, Ron; Okujeni, Akpona; Roscher, Ribana; Wagner, Paul D.; Waske, Björn

doi:10.1109/jstars.2017.2652726

Cited by 22 publications

(17 citation statements)

References 61 publications

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“…First, the created STOs are the overall representation of the changes in urban greenness associated with the mixed effect from the fraction of vegetated land cover and other land cover types, known as endmembers [19]. Urban land cover is often highly fragmented as the result of the long-term interactions from natural and anthropogenic factors, which leads to a high within-class variance or mixed-pixel problem for many mapped land cover units [19,61,62]. Xian et al [63] stated that a single pixel in remote sensing imagery in urban areas is often mixed and composed of several land-cover/land-use types.…”

Section: Scale Consideration In Lsaa-tcmentioning

confidence: 99%

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Sha

Ali

Wang

et al. 2018

IJGI

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Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.

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Section: Scale Consideration In Lsaa-tcmentioning

confidence: 99%

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Sha

Ali

Wang

et al. 2018

IJGI

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“…We can also compare our results to Rosentreter et al (2017), who use more sophisticated classification and regression techniques to reconstruct the EnMAP data with the same spectral library Lib. When using Import or Support Vector Machines the overall MAE (8, 65% and 10, 22%) is slightly worse than AA-R.…”

Section: Sub-pixel Quantificationmentioning

confidence: 99%

“…In this context, several approaches have been developed comprising regression approaches (Okujeni et al, 2016b;Priem et al, 2016), probabilistic classification methods (Rosentreter et al, 2017;Suess et al, 2014), and the usage of spectral libraries for spectral mixture analysis (Somers et al, 2011;Powell et al, 2007). An overview of a wide variety of unmixing approaches can be found in e.g.…”

Section: Introductionmentioning

confidence: 99%

“…While the latter approach needs a spectral library containing elementary spectra of known materials, the first two approaches also require mixed spectra for learning an appropriate model. These mixed spectra can be derived from the image using information about known mixed pixels, or from synthetically mixed pixel, as been presented in Rosentreter et al (2017) and Okujeni et al (2016b).…”

Section: Introductionmentioning

confidence: 99%

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Archetypal Analysis for Sparse Representation-Based Hyperspectral Sub-Pixel Quantification

Drees

Roscher

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper focuses on the quantification of land cover fractions in an urban area of Berlin, Germany, using simulated hyperspectral EnMAP data with a spatial resolution of 30m×30m. For this, sparse representation is applied, where each pixel with unknown surface characteristics is expressed by a weighted linear combination of elementary spectra with known land cover class. The elementary spectra are determined from image reference data using simplex volume maximization, which is a fast heuristic technique for archetypal analysis. In the experiments, the estimation of class fractions based on the archetypal spectral library is compared to the estimation obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum of fractions and the number of used elementary spectra. We will show, that a collection of archetypes can be an adequate and efficient alternative to the spectral library with respect to mentioned criteria.

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“…Rich spectral-spatial information is widely used in HSIs for scene recognition [2], regional variation of urban areas [3], and classification of features [4][5][6]. Classification of HSIs for ground objects can be widely used in precision agriculture [7], urban mapping [8], and environmental monitoring [9]. As such, classification has attracted much attention, and a wide variety of methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance

2019

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Today, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, such approaches are still hampered by long training times. Traditional spectral–spatial hyperspectral image classification only utilizes spectral features at the pixel level, without considering the correlation between local spectral signatures. Our article has tested a novel hyperspectral image classification pattern, using random-patches convolution and local covariance (RPCC). The RPCC is an effective two-branch method that, on the one hand, obtains a specified number of convolution kernels from the image space through a random strategy and, on the other hand, constructs a covariance matrix between different spectral bands by clustering local neighboring pixels. In our method, the spatial features come from multi-scale and multi-level convolutional layers. The spectral features represent the correlations between different bands. We use the support vector machine as well as spectral and spatial fusion matrices to obtain classification results. Through experiments, RPCC is tested with five excellent methods on three public data-sets. Quantitative and qualitative evaluation indicators indicate that the accuracy of our RPCC method can match or exceed the current state-of-the-art methods.

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Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression

Cited by 22 publications

References 61 publications

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Archetypal Analysis for Sparse Representation-Based Hyperspectral Sub-Pixel Quantification

A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance

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