A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

Laliberte, Andrea S.; Browning, Dawn M.; Rango, A.

doi:10.1016/j.jag.2011.05.011

Cited by 126 publications

(71 citation statements)

References 27 publications

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“…Twelve of the 39 images available for the training period were removed. We evaluated the separability of the F and NF distributions for NDVIMD and HVHHmt using the normalized Jeffries-Matusita distance (JM) [73]. JM has a finite dynamic range from 0 (inseparable) to 2 (separable).…”

Section: Deriving F and Nf Pdfsmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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Section: Deriving F and Nf Pdfsmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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“…These products have certainly improved our understanding of regional and global land cover distribution and their change status. Unfortunately, due to the relatively low spatial resolution, they are insufficient for detailed land cover mapping for those areas with complex and high heterogeneous landscapes such as the urban environment, which is featured by its small-sized elements (e.g., buildings, roads and lawns) combined with complicated spatial patterns [13,14]. This limitation greatly hinders researchers and policy makers from taking full advantage of these maps to support their various and particular applications.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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“…Features were first selected to construct a classification decision tree (De'ath and Fabricius 2000; Laliberte et al, 2012). The resultant decision tree suggested that density and shape index were the two most efficient features, which were then used to develop the ruleset to identify particles from SEM micrographs.…”

Section: Object-based Classificationmentioning

confidence: 99%

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

Yan

Lin

Zhou

et al. 2016

Journal of Environmental Sciences

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It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope (SEM) micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found (1) Rosa xanthina Lindl (shrub species) had greater retention efficiency than Broussonetia papyrifera (broadleaf species) and Pinus bungeana Zucc.(coniferous species), in terms of particle number and particle area cover. (2) 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical. (3) 32.4%-74.1% of the particles were generated from bare soil and construction activities, and 15.5%-23.0% were mainly from vehicle exhaust and cooking fumes.

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A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

Cited by 126 publications

References 27 publications

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

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