Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data

Schuster, Christian; Förster, Michael; Kleinschmit, Birgit

doi:10.1080/01431161.2012.666812

Cited by 202 publications

(152 citation statements)

References 24 publications

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“…The overall accuracy, producer's accuracy and user's accuracy [65] were determined. Additionally, the F1 score (Equation (6)) [70,71], which combines producer's and user's accuracy into a composite measure, was computed for each class. This measure enables a better assessment of class-wise accuracies.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

et al. 2014

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Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%-15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

et al. 2014

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“…Thanks to its state-of-the-art specifications, Sentinel-2 [12,13] was designed for a variety of land monitoring applications such as water detection [14], mapping built-up areas [15] and crop type and tree species identification [16]. In addition to its spatial resolution, its payload offers thirteen spectral bands from Blue to SWIR, including Red-edge bands which have already proved to be useful for forest stress monitoring [17], land use and land cover mapping [18,19] and biophysical variable retrieval [20][21][22][23]. In fragmented landscapes, the components of the ecological networks are generally small, i.e., sub-pixel targets undetected by conventional multi-spectral classification methods [24,25].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

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Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

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“…These machine-learning classification algorithms have been used in complex landscapes [54,82]; notably, the performances of RF, SVM, boosted classification and regression trees, and KNN have been compared in classifying surface-mined and reclamation land [24,26,27,54]. Although RF and SVM often showed similar classification abilities and vary in different classification tasks, it was reported that SVM can provide more accurate classifications of surface mining and mine reclamation areas than the RF algorithm in complex surface-mined landscapes.…”

Section: Land Cover Classification Algorithmsmentioning

confidence: 99%

“…Firstly, some sensors not only feature increased spatial resolution but also the number of spectral bands, such as RapidEye, Worldview-3, and WorldView-4. The red-edge band of RapidEye satellite imagery can be used to identify the type and growth state of vegetation [53,[82][83][84], which may be useful for fine-scale LCCMA. Some studies have also assessed the effect of the red-edge band on some special classifications [53,82,84].…”

Section: Unitization Of New Satellite Sensorsmentioning

confidence: 99%

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Chen

et al. 2017

Remote Sensing

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Abstract:Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

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Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data

Cited by 202 publications

References 24 publications

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

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