Search results for "cognition."

SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information

2018

Remote-sensing focusing on image classification knows a large progress and receives the attention of the remote-sensing community day by day. Combining many kinds of extracted features has been successfully applied to High resolution urban satellite images using support vector machine (SVM). In this paper, we present a methodology that is promoting a performed classification by using pixel-wise SURF description features combined with spectral information in Cielab space for the first time on common scenes of urban imagery. The proposed method gives a promising classification accuracy when compared with the two types of features used separately.

SAR Image Classification Combining Structural and Statistical Methods

2011

The main objective of this paper is to develop a new technique of SAR image classification. This technique combines structural parameters, including the Sill, the slope, the fractal dimension and the range, with statistical methods in a supervised image classification. Thanks to the range parameter, we define the suitable size of the image window used in the proposed approach of supervised image classification. This approach is based on a new way of characterising different classes identified on the image. The first step consists in determining relevant area of interest. The second step consists in characterising each area identified, by a matrix. The last step consists in automating the pr…

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

2015

In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-easte…

2016

This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north-eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence-only-based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of fai…

Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

2017

Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…

Predicting year of plantation with hyperspectral and lidar data

2017

This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sens…

Give me a sample of air and I will tell which species are found from your region : Molecular identification of fungi from airborne spore samples

2017

Fungi are a megadiverse group of organisms, they play major roles in ecosystem functioning and are important for human health, food production and nature conservation. Our knowledge on fungal diversity and fungal ecology is however still very limited, in part because surveying and identifying fungi is time demanding and requires expert knowledge. We present a method that allows anyone to generate a list of fungal species likely to occur in a region of interest, with minimal effort and without requiring taxonomical expertise. The method consists of using a cyclone sampler to acquire fungal spores directly from the air to an Eppendorf tube, and applying DNA barcoding with probabilistic specie…

Howl variation across Himalayan, North African, Indian, and Holarctic wolf clades: tracing divergence in the world’s oldest wolf lineages using acous…

2017

Abstract Vocal divergence within species often corresponds to morphological, environmental, and genetic differences between populations. Wolf howls are long-range signals that encode individual, group, and subspecies differences, yet the factors that may drive this variation are poorly understood. Furthermore, the taxonomic division within the Canis genus remains contended and additional data are required to clarify the position of the Himalayan, North African, and Indian wolves within Canis lupus. We recorded 451 howls from the 3 most basal wolf lineages—Himalayan C. lupus chanco—Himalayan haplotype, North African C. lupus lupaster, and Indian C. lupus pallipes wolves—and present a howl ac…

2021

Both plants and animals are endowed with sophisticated innate immune systems to combat microbial attack. In these multicellular eukaryotes, innate immunity implies the presence of cell surface receptors and intracellular receptors able to detect danger signal referred as damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs). Membrane-associated pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs), C-type lectin receptors (CLRs), receptor-like kinases (RLKs), and receptor-like proteins (RLPs) are employed by these organisms for sensing different invasion patterns before triggering antimicrobial defenses that can be associated with…

2017

Males compete over mating and fertilization, and often harm females in the process. Inclusive fitness theory predicts that increasing relatedness within groups of males may relax competition and discourage male harm of females as males gain indirect benefits. Recent studies in Drosophila melanogaster are consistent with these predictions, and have found that within-group male relatedness increases female fitness, though others have found no effects. Importantly, these studies did not fully disentangle male genetic relatedness from larval familiarity, so the extent to which modulation of harm to females is explained by male familiarity remains unclear. Here we performed a fully factorial de…