Introduction to the Special Section on Image Information Mining for Earth Observation Data

Datcu, Mihai; D'Elia, Sergio; King, Roger L.; Bruzzone, Lorenzo

doi:10.1109/tgrs.2007.894401

Cited by 14 publications

(4 citation statements)

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“…About 100 operating satellites are currently dedicated to Earth observation (Gail, 2007; accessed June 2008 http://www.asprs.org/news/satellites/satellites.html or http://directory.eoportal.org/d_art.php?type=30700000), and image archives continue to grow at several terabytes per day (Datcu et al, 2007). Cancer studies that require land cover information generally need high-resolution (<10 m) or mid-resolution (10–100 m) imagery with a long-term image archive (20 + years).…”

Section: Discussionmentioning

confidence: 99%

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

Maxwell

Meliker

Goovaerts

2009

J Expo Sci Environ Epidemiol

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In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

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Section: Discussionmentioning

confidence: 99%

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

Maxwell

Meliker

Goovaerts

2009

J Expo Sci Environ Epidemiol

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“…Ersahin et al [32] used a graph partitioning algorithm to implicitly capture some of the underlying contextual information. However, none of these methods is capable of exploiting high-level context [33] such as the relationships between different kinds of objects and scenes (e.g., the fact that ships usually co-occur with water).…”

Section: Related Workmentioning

confidence: 99%

SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models

Yang

Dai²,

Triggs

et al. 2012

IEEE Trans. on Image Process.

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We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models-the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent-child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards-backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training.

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“…That means that end users can access and use various remotely sensed data to achieve different operations and apply in much more actual applications too. In the process of image processing, how to quickly and accurately evaluate the amount of information of images has become one of the critical factors of improving the utilization ratio of image data, reducing data cost and meeting in some degree the timeliness of image processing (Datcu et al, 2007;Quartullinet al, 2013;Xia et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An Approach for Evaluating the Information Content of Remote Sensing Images

Fang¹,

Zhou²

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Due to being affected by the rapid development of open science and the increasing popularity of mobile devices (e.g., smartphones), remote sensing data as frequently used data sources are broadly applied to our daily life. At the same time, remote sensing data collection also presents a trend of popularization. To improve the utilization efficiency and availability of the obtained diversified remote sensing data, we propose a novel evaluation method based on information theory and scatterplot mapping model, i.e., geometrical mapping entropy (GME). The goal is to construct a unified model of measurement to be much more effectively and accurately evaluate the information content and quality of remotely sensed imagery. Different experimental data are used to verify the performance of the proposed method, i.e., a group of the dataset that contains different four types of images; the other group of image data contains the images with different modalities and different imaging times (2016–05, 2017–08, 2018–04, and 2018–06). Experimental results indicate that the proposed approach can better characterize the spectrum features and spatial structural features contained in images and visual perception information. Additionally, it can also reflect the difference in the quality of different modality images, especially the effect for the images that contain clouds or poor lighting conditions, is better.

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Introduction to the Special Section on Image Information Mining for Earth Observation Data

Cited by 14 publications

References 0 publications

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models

An Approach for Evaluating the Information Content of Remote Sensing Images

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