Spatial thresholds, image-objects, and upscaling: A multiscale evaluation

Hay, Geoffrey J.; Niernann, K.O.; Goodenough, D.G.

doi:10.1016/s0034-4257(97)81622-7

Cited by 105 publications

(53 citation statements)

References 27 publications

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“…The pan-sharpening enhanced the information of the multi-spectral bands because of higher spatial resolution of the panchromatic band and more details of ground objects. Moreover, several studies have indicated that the window average method is most accurate among the widely used up-scaling methods used to capture the texture characteristics of vegetated areas in the up-scaled images [38][39][40]. In this study, on the other hand, resampling was performed twice on the multi-spectral bands, which changed the values of the image pixels and might have led to uncertainty of classification.…”

Section: Discussionmentioning

confidence: 99%

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Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

et al. 2017

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Abstract:There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys-Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification.

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Section: Discussionmentioning

confidence: 99%

“…Because of limited space and time, the amount of the uncertainty and its impact on the classification accuracy were not discussed in this study. However, based on previous studies [38][39][40], the uncertainty was limited and could be ignored.…”

Section: Discussionmentioning

confidence: 99%

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

et al. 2017

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“…Object-oriented image analysis is based on the paradigm that image-objects are the fundamental entity in remote sensing imagery. The term image-objects refers to individually resolvable entities located within a digital image that are perceptually generated from pixel groups [13]. From this, it follows that attributes such as shape, size and mutual relationships between objects can be used in the classification process.…”

Section: Image Analysismentioning

confidence: 99%

Remote Sensing in Social Science Research~!2009-12-28~!2010-03-26~!2010-06-25~!

Hall¹

2010

TORMSJ

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Abstract:Since the early days of satellite remote sensing in the 1950's, accessibility, quality, and scope of remote sensing image data has been continuously improving, making it a rich data source with a wide range of applications. Today, the use of remote sensing techniques and data is commonplace within many disciplines in the natural sciences. Although there are quite a few examples of remote sensing to be found in the social sciences, developments here have, on the whole, been less pronounced. This paper investigates 1) how remote sensing data has been put to use in social science studies, and 2) how social science could better utilize the huge potential of remote sensing data. The first part of the paper gives an overview of existing types of remote sensing techniques and data collection. The second part consistsof a review of social science applications of remote sensing data. In the conclusions it is argued that remote sensing data is at its most valuable in the social sciences when used in combination with traditional methods such as surveys, public records, interviews and direct observation.

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“…One relatively common form of multi-scale analysis is upscaling, whereby detailed local measurements are extrapolated over wider areas (Hay et al 1997, Anderson et al 2004, Wang et al 2004. Conversely, as da Silva et al (2005) demonstrate, it is also possible to 'downscale' and infer local detail from wider-scale information.…”

Section: Spatial Variabilitymentioning

confidence: 99%

On scales and dynamics in observing the environment

Aplin

2006

International Journal of Remote Sensing

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Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on 'Scales and Dynamics in Observing the Environment'. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of 'Scales and Dynamics in Observing the Environment'.

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Spatial thresholds, image-objects, and upscaling: A multiscale evaluation

Cited by 105 publications

References 27 publications

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

Remote Sensing in Social Science Research~!2009-12-28~!2010-03-26~!2010-06-25~!

On scales and dynamics in observing the environment

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