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Authors = Noel Richard ORCID = 0000-0002-6891-0540

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Open AccessArticle Toward Content-Based Hyperspectral Remote Sensing Image Retrieval (CB-HRSIR): A Preliminary Study Based on Spectral Sensitivity Functions
Remote Sens. 2019, 11(5), 600; https://doi.org/10.3390/rs11050600
Received: 21 January 2019 / Revised: 27 February 2019 / Accepted: 7 March 2019 / Published: 12 March 2019
Viewed by 437 | PDF Full-text (3630 KB) | HTML Full-text | XML Full-text
Abstract
With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI) produced by different types of imaging sensors, analyzing and retrieving these images require effective image description and quantification techniques. Compared to remote sensing RGB images, HSI data contain hundreds of spectral bands [...] Read more.
With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI) produced by different types of imaging sensors, analyzing and retrieving these images require effective image description and quantification techniques. Compared to remote sensing RGB images, HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowing profile materials and organisms that only hyperspectral sensors can provide. In this article, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images. The main goal of such representation is to improve image content recognition by focusing the processing on only the most relevant spectral channels. The underlying hypothesis is that for a given category, the content of each image is better extracted through a specific set of spectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-Based Image Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remote sensing community, specifically designed for Hyperspectral remote sensing retrieval and classification. Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtained retrieval results prove that the physical measurements and optical properties of the scene contained in the HSI contribute in an accurate image content description than the information provided by the RGB image presentation. Full article
(This article belongs to the Special Issue Image Retrieval in Remote Sensing)
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Graphical abstract
Full article ">Figure 1
<p>Block diagram of the proposed framework.</p>
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<p>Examples from the ICONES-Hyperspectral Satellite Imaging dataset (RGB-display).</p>
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<p>Different levels of Spectral Sensitivity Functions (SSFs).</p>
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<p>Best SSF for each class according to the retrieval performance.</p>
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<p>Examples of RGB-display images (form left to right) of Mountain, Snow, Forest, and Dense-Urban categories (line 1) with their corresponding spectra (line 2) and best triplet of SSFs (line 3).</p>
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Open AccessArticle Ecoregional Patterns of Spruce Budworm—Wildfire Interactions in Central Canada’s Forests
Forests 2018, 9(3), 137; https://doi.org/10.3390/f9030137
Received: 15 February 2018 / Revised: 6 March 2018 / Accepted: 13 March 2018 / Published: 14 March 2018
Cited by 3 | Viewed by 1190 | PDF Full-text (3543 KB) | HTML Full-text | XML Full-text
Abstract
Wildfires and outbreaks of the spruce budworm, Choristoneura fumiferana (Clem.), are the two dominant natural disturbances in Canada’s boreal forest. While both disturbances have specific impacts on forest ecosystems, it is increasingly recognized that their interactions also have the potential for non-linear behavior [...] Read more.
Wildfires and outbreaks of the spruce budworm, Choristoneura fumiferana (Clem.), are the two dominant natural disturbances in Canada’s boreal forest. While both disturbances have specific impacts on forest ecosystems, it is increasingly recognized that their interactions also have the potential for non-linear behavior and long-lasting legacies on forest ecosystems’ structures and functions. Previously, we showed that, in central Canada, fires occurred with a disproportionately higher frequency during a ‘window of opportunity’ following spruce budworm defoliation. In this study, we use Ontario’s spatial databases for large fires and spruce budworm defoliation to locate where these two disturbances likely interacted. Classification tree and Random Forest procedures were then applied to find how spruce budworm defoliation history, climate, and forest conditions best predict the location of such budworm–fire interactions. Results indicate that such interactions likely occurred in areas geographically bound by hardwood content in the south, the prevalence of the three major spruce budworm host species (balsam fir, white spruce and black spruce) in the north, and climate moisture in the west. The occurrence of a spruce budworm–fire interaction inside these boundaries is related to the frequency of spruce budworm defoliation. These patterns provide a means of distinguishing regions where spruce budworm attacks are likely to increase fire risk. Full article
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Figure 1

Figure 1
<p>(<bold>A</bold>) Frequency (number of years) of moderate to severe defoliation by spruce budworm between 1941 and 2005 on a 10-km grid; (<bold>B</bold>) Average climate moisture index (cm/year) between 1 November and 31 October on a 10-km grid from 1941 to 2003. (See text for calculating algorithm).</p>
Full article ">Figure 1 Cont.
<p>(<bold>A</bold>) Frequency (number of years) of moderate to severe defoliation by spruce budworm between 1941 and 2005 on a 10-km grid; (<bold>B</bold>) Average climate moisture index (cm/year) between 1 November and 31 October on a 10-km grid from 1941 to 2003. (See text for calculating algorithm).</p>
Full article ">Figure 2
<p>‘Likely interactions’ (in red) of spruce budworm defoliation and large (&gt;2 km<sup>2</sup>) fires in the spruce budworm belt (gray) mapped on a 10-km grid for 1941–2005.</p>
Full article ">Figure 3
<p>Classification tree of the presence (1) or absence (0) of ‘likely interaction’ between large fires and spruce budworm defoliation in Ontario’s spruce budworm belt (<xref ref-type="fig" rid="forests-09-00137-f001">Figure 1</xref>A) from 1941 to 2005. Five splits (horizontal bars) and six leaves (circles numbered 1–6 from left to right) are shown. A variable is shown above each bar followed by the two inequality signs, ‘&gt;’ and ‘&lt;’. The inequality sign on the left (right) applies to the left (right) end of the bar. The variables are the number of years of moderate–severe defoliation (sbwfreq), the average climate moisture index (cmi_ave), the percentage of the total basal area that is hardwood (hw) or balsam fir, white spruce and black spruce combined (fbswsb). The ‘0’ or ‘1’ directly below the circle at each leaf and leading the second line above each bar indicate whether the previous split classified this group of cells as having conditions conducive to the presence (1) or absence (0) of interaction. The ‘0’ or ‘1’ is followed by the group size (# cells) and then, for the bars, the correct classification %, and for leaves, parentheses enclosing the numbers of cells where absence/presence [of a “likely interaction”] is predicted. See text for further explanation.</p>
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<p>Areas uniquely associated with each leaf of the classification tree in <xref ref-type="fig" rid="forests-09-00137-f003">Figure 3</xref>.</p>
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<p>Importance of the explanatory variables used to construct the classification tree (<xref ref-type="fig" rid="forests-09-00137-f003">Figure 3</xref>), as assessed by the randomForest procedure. These variables are the average climate moisture index (cmi_ave); the number of years of moderate–severe defoliation (sbwfreq); and the percentages of the total basal area, that is, hardwood (hw), balsam fir and white spruce combined (fbsw), or balsam fir, white spruce and black spruce combined (FbSwSb).</p>
Full article ">Figure 6
<p>Classification tree of the presence (1) or absence (0) of the interaction between fire and spruce budworm defoliation as a function of just the two most important explanatory variables (<xref ref-type="fig" rid="forests-09-00137-f005">Figure 5</xref>) from the data used to grow the tree shown in <xref ref-type="fig" rid="forests-09-00137-f003">Figure 3</xref>. These two variables are the frequency of defoliation by spruce budworm (sbwfreq) and the annual average climate moisture index (cmi_ave). See the caption to <xref ref-type="fig" rid="forests-09-00137-f003">Figure 3</xref> for additional detail.</p>
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<p>Distributions of the importance rankings of the explanatory variables in 50 classification trees calculated on random samples of the original dataset. Importance was measured by the mean decrease accuracy in the randomForests procedure.</p>
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<p>Probability of interaction between spruce budworm and fire based on predictions from 50 classification trees calculated on random samples of the original dataset.</p>
Full article ">

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