Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

Poursanidis, Dimitris; Chrysoulakis, Nektarios; Mitraka, Zina

doi:10.1016/j.jag.2014.09.010

Cited by 110 publications

(74 citation statements)

References 18 publications

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“…An interesting pattern emerged here: ML was generally more accurate at the coarser spatial resolutions, but this trend was reversed at the finer resolutions with SVM becoming superior ( Figure 6). This outcome is likely explained by the quality of the training data at the different resolutions and the fact that SVMs are better able than ML to handle complex, noisy data (i.e., as may occur at finer resolutions) [18]. As might be expected, this finding was most pronounced at the finest, 2 m, resolution, where differences between SVM and ML were generally statistically significant ( Figure 7).…”

Section: Classifier Choicementioning

confidence: 69%

“…(Indeed, these advancements in spectral capability are not restricted to VHR instruments; the most recent Landsat sensor, Operational Land Imager (OLI), has ten multispectral bands, including new coastal, cirrus and thermal infrared 2 bands.) These enhanced spectral properties may prove especially valuable for urban mapping, enabling subtly varying spectral classes to be identified [18]. This advantage is likely to be most pertinent where detailed classification schema are involved, for instance when identifying many different land cover classes in complex urban environments.…”

Section: Enhanced Spectral Capabilitiesmentioning

confidence: 99%

“…Traditionally, urban classification has been conducted using pixel-based approaches, whereby land cover classes are allocated to each individual pixel [18,19]; and historically most such analysis has employed statistical parametric classifiers such as the maximum likelihood (ML) algorithm [15,20,21]. Though ML classification is a perfectly valid method, it makes certain statistical assumptions about the data, and the nature of VHR imagery can mean that it is difficult to honour these assumptions.…”

Section: Pixel-based Versus Object-based Classificationmentioning

confidence: 99%

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Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

2016

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Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern "very high resolution" or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.

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Section: Classifier Choicementioning

confidence: 69%

Section: Enhanced Spectral Capabilitiesmentioning

confidence: 99%

Section: Pixel-based Versus Object-based Classificationmentioning

confidence: 99%

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Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

2016

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“…By reducing the dataset's size, we observed that the RMSE does not change. DSMs are incorporated in the process of image classification in urban/rural areas, for a holistic approach of land use/cover with improved results [21], or a targeted image classification, as is the case of urban green vegetation [3], but also in the process of the extraction of buildings [22,23], which resulted in more accurate results. The incorporation of DSMs in mapping elements in urban environments can benefit the current research by improving the results by incorporating texture and shape information apart from the spectral information [24,25].…”

Section: Resultsmentioning

confidence: 99%

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Panagiotakis

Chrysoulakis

Charalampopoulou

et al. 2018

IJGI

Self Cite

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Abstract:We present an accurate digital surface model (DSM) derived from high-resolution Pleiades-1B 0.5 m panchromatic tri-stereo images, covering an area of 400 km 2 over the Athens Metropolitan Area. Remote sensing and photogrammetry tools were applied, resulting in a 1 m × 1 m posting DSM over the study area. The accuracy of the produced DSM was evaluated against measured elevations by a differential Global Positioning System (d-GPS) and a reference DSM provided by the National Cadaster and Mapping Agency S.A. Different combinations of stereo and tri-stereo images were used and tested on the quality of the produced DSM. Results revealed that the DSM produced by the tri-stereo analysis has a root mean square error (RMSE) of 1.17 m in elevation, which lies within the best reported in the literature. On the other hand, DSMs derived by standard analysis of stereo-pairs from the same sensor were found to perform worse. Line profile data showed similar patterns between the reference and produced DSM. Pleiades tri-stereo high-quality DSM products have the necessary accuracy to support applications in the domains of urban planning, including climate change mitigation and adaptation, hydrological modelling, and natural hazards, being an important input for simulation models and morphological analysis at local scales.

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“…The classification accuracy depends on the spatial and spectral resolution using image, classification methods, training data, the seasonal variability in vegetation cover and crop types, soil moisture conditions, etc. (Poursanidis et al 2015;Ustuner and Sanli, 2015). All these factors limit the accuracy of image classification.…”

Section: Introductionmentioning

confidence: 99%

Testing the Potential of Vegetation Indices for Land Use/Cover Classification Using High Resolution Data

Kuzucu

Balçık

2017

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

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ABSTRACT:Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

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Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

Cited by 110 publications

References 18 publications

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Testing the Potential of Vegetation Indices for Land Use/Cover Classification Using High Resolution Data

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