Assumptions implicit in remote sensing data acquisition and analysis

Duggin, Michael J.; Robinove, Charles J.

doi:10.1080/01431169008955124

Cited by 108 publications

(45 citation statements)

References 29 publications

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“…In each time slot, two images were acquired for spring (April) and summer (August) to detect phenological differences in the various LULC and especially the most dynamic ones such as meadows, phrygana and arable land. When using multi-temporal images, calibration and georeferencing are necessary prior to classification and change detection [41]. In our case, we used images that were pre-processed at Level 1 (geometric corrections in UTM WGS84 projection), and we applied an absolute atmospheric correction using Dark Object Subtraction algorithm (DOS) in ENVI 5.5 software.…”

Section: Object-oriented Classification For Lulc Change Analysismentioning

confidence: 99%

Mapping and Understanding the Dynamics of Landscape Changes on Heterogeneous Mediterranean Islands with the Use of OBIA: The Case of Ionian Region, Greece

Kefalas

Poirazidis

Xofis³

et al. 2018

Sustainability

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Mediterranean islands contain heterogeneous landscapes, resulting from the complex interactions between natural and anthropogenic processes, and have significant ecological and conservation importance. They are vulnerable systems to global change and the monitoring of changes, induced by the interacting environmental drivers, is of particular importance for applying a sustainable management regime. The aim of this study was to detect and analyze the landscape dynamics and changes in landscape composition over a 30-year period on the Ionian Islands of Western Greece. State-of-the-art object-oriented image analysis on freely available remote sensing data such as Landsat images was employed achieving final mapping products with high spatial and thematic accuracy (over than 85%), and a transferable classification scheme. The main drivers of environmental change are tourism and associated activities, wildfires and livestock breeding which act in different ways and intensities within and between the islands. The repopulation of those islands, after a period of significant depopulation from the 1940s to the 1980s, and the boom of tourism since the mid-1970s prevented further land abandonment and the recultivation of abandoned land which indicates that tourism and agriculture can be complementary rather than competing economic sectors. Despite the significant increase of tourism, a general trend was observed towards increasing cover of high-density vegetation formations, such as shrublands and forests. At the same time, wildfires, which are in some cases associated with livestock breeding, continue to be an important vegetation degradation factor preventing further ecosystem recovery on the study islands.

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Section: Object-oriented Classification For Lulc Change Analysismentioning

confidence: 99%

Mapping and Understanding the Dynamics of Landscape Changes on Heterogeneous Mediterranean Islands with the Use of OBIA: The Case of Ionian Region, Greece

Kefalas

Poirazidis

Xofis³

et al. 2018

Sustainability

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“…Based on the complexity of (1) which involves the optimization of a global distribution model of the image and due to the equivalence of MRF and Gibbs random field (Duggin and Robinove, 1990), (Geman and Geman, 1984), this optimization problem can be simplified and resolved by minimizing the sum of local posterior energies (2) (Geman and Geman, 1984):…”

Section: The Potts Mrf Modelmentioning

confidence: 99%

Smoothing Parameter Estimation for Markov Random Field Classification of non-Gaussian Distribution Image

Aghighi

Trinder

Wang

et al. 2014

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

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Commission VII, WG VII/4 KEY WORDS: Markov random field, smoothing parameter, SVM, non-Gaussian distribution ABSTRACT:In the context of remote sensing image classification, Markov random fields (MRFs) have been used to combine both spectral and contextual information. The MRFs use a smoothing parameter to balance the contribution of the spectral versus spatial energies, which is often defined empirically. This paper proposes a framework to estimate the smoothing parameter using the probability estimates from support vector machines and the spatial class co-occurrence distribution. Furthermore, we construct a spatially weighted parameter to preserve the edges by using seven different edge detectors. The performance of the proposed methods is evaluated on two hyperspectral datasets recorded by the AVIRIS and ROSIS and a simulated ALOS PALSAR image. The experimental results demonstrated that the estimated smoothing parameter is optimal and produces a classified map with high accuracy. Moreover, we found that the Canny-based edge probability map preserved the contours better than others.

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“…The significant problem in the atmospheric correction of remotely sensed data is the difficulty of obtaining accurate values for those parameters described in the radiative transfer equation (Kaufman and Sendra 1988) or the limited availability of the atmospheric correction parameters in the analysis of most images (Duggin and Robinove 1990). The basic philosophy of atmospheric correction is to determine the optical characteristics of the atmosphere and then to apply this to correct the satellite image data.…”

Section: Atmospheric Correction Of Satellite Imagerymentioning

confidence: 99%

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

Hadjimitsis

2009

Air Qual Atmos Health

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The aim of this study is to present and apply the proposed algorithm on archived time series Landsat TM over an urban area in the vicinity of Heathrow Airport (UK) acquired in 1984-1986 and to two up-to-dated Landsat TM images in the vicinity of Paphos Airport in Cyprus acquired in July-August 2008. The monitoring of aerosol concentrations becomes a high environmental priority particularly in urban areas. The proposed algorithm has been developed to allow the quantification of the aerosol optical thickness (AOT) over land. The algorithm compares multitemporal satellite data sets and evaluates radiometric alterations due to the optical atmospheric effects of aerosols. Novel features of this algorithm which is based on the application of radiative transfer calculations are the inclusion of applying iteration procedures for selecting the suitable object for determining the aerosol optical thickness and the automatic division into working grid cells. AOT retrieval from Landsat TM band 1 images of Cyprus has been crossvalidated satisfactorily by comparing AOT values found from handheld MICROTOPS II sun photometer.

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Assumptions implicit in remote sensing data acquisition and analysis

Cited by 108 publications

References 29 publications

Mapping and Understanding the Dynamics of Landscape Changes on Heterogeneous Mediterranean Islands with the Use of OBIA: The Case of Ionian Region, Greece

Mapping and Understanding the Dynamics of Landscape Changes on Heterogeneous Mediterranean Islands with the Use of OBIA: The Case of Ionian Region, Greece

Smoothing Parameter Estimation for Markov Random Field Classification of non-Gaussian Distribution Image

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

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