A Content Based Region Separation and Analysis Approach for SAR Image Classification

SAR images are the images captured through satellite or radar to monitor the specific geographical area or to extract any information regarding the geographical structure. This information can be used to recognize the land areas or regions with specific features such as identification of water area or flood area etc. But the images captured from satellite covers larger land regions with multiple scene pictures. To recognize the specific land area, it is required to process all the images with defined constraints to identify the particular region. The images or the image features can be trained under some classification method to categorize the land regions. There are various supervised and unsupervised classification methods to classify the SAR images. But the SAR images are high resolution images with multiple region types in same images. Because of this, the existing methods are not fully capable to classify the regions accurately. There is the requirement of more effective classification that can identify the land regions more adaptively

A review of UAV Visual Detection and Tracking Methods

This paper presents a review of techniques used for the detection and tracking of UAVs or drones. There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detection and tracking. Hybrid detection techniques are also presented. The paper is a quick reference for a wide spectrum of methods that are used in the drone detection process.Comment: 10 page

Decline in the strength of genetic controls on aspen environmental responses from seasonal to century‐long phenomena

Understanding intra-specific variation in climate sensitivity could improve the prediction of tree responses to climate change. We attempted to identify the degree of genetic control of tree phenology and growth of trembling aspen (Populus tremuloides Mchx.) in a natural stand of this species in northwestern Quebec. We mapped and genotyped 556 aspen trees growing within the plot, using seven nuclear microsatellite loci for clone identification. We selected 13 clones (n of trees per clone >5, in total 350 trees) and evaluated the explanatory power of clone identity in (a) variability of spring leaf phenology and (b) short- and long-term growth responses. The clone's identity explained 43% of the variability in spring leaf phenology, between 18% and 20% of variability in response to monthly climate variables significantly affecting growth, between 8% and 26% of growth response to insect outbreaks, and 12% in the long-term growth rates. Strong clonal control of aspen phenology and moderate control of growth responses to monthly weather do not result in an equally large impact on long-term growth rates. The result suggests an important role of environmental extremes and within community interactions as factors averaging aspen growth performance at the stand level

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/ and H/A/ Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and . The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/ space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change.Fil: Morandeira, Natalia Soledad. Universidad Nacional de San Martín; ArgentinaFil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Faccinetti, Claudia. Agenzia Spaziale Italiana; ItaliaFil: Kandus, Patricia. Universidad Nacional de San Martín; Argentin

Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones

The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors

Employing NIR-SWIR hyperspectral imaging to predict the smokiness of Scotch whisky

Scotch Whisky makes a significant contribution to the UK's food and drinks export. The flavour of this high quality spirit is derived naturally from the whisky making process, with smoky aromas being a key character of certain Scotch whiskies. The level of smokiness is determined by the amount of phenolic compounds in the spirit. Phenols are introduced by exposing the barley malt to peat smoke during the kilning process. The current techniques to determine the levels of phenols, such as High Performance Liquid Chromatography (HPLC), are time consuming as they require distillation of the malt prior to analysis. To speed up this process and enable real-time detection before processing, the possibilities of Near-infrared to Short-wave-infrared (NIR-SWIR) Hyperspectral Imaging (HSI) to detect these phenols directly on malted barley are explored. It can be shown that via regression analysis, various levels of phenol concentration used as working levels for whisky production could be estimated to a satisfying degree. To further optimise industrial application, a hyperspectral band selection algorithm is applied that yields good results and reduces computational cost and may open possibilities to employ multispectral rather than hyperspectral cameras in future applications

Vector Quantization Codebook Design and Application Based on the Clonal Selection Algorithm

In the area of digital image compression, the vector quantization algorithm is a simple, effective and attractive method. After the introduction of the basic principle of the vector quantization and the classical algorithm for vector quantization codebook design, the paper, based on manifold distance, presents a clonal selection code book design method, using disintegrating method to produce initial code book and then to obtain the final code book through optimization with the clonal selection cluster method based on the manifold distance. Through experiment, based on manifold distance, compared the clonal selection codebook design algorithm (MDCSA) with the hereditary codebook design algorithm and LBG algorithm. According to the result of the experiment, MDCSA is more suitable for the evolution algorithm of the image compression

Multiscale classification of very high resolution SAR images of urban areas by Markov random fields, copula functions, and texture extraction

National audienceThis paper addresses the problem of classifying very high resolution synthetic aperture radar (SAR) images of urban areas by using a supervised Bayesian classification method via a contextual hierarchical approach. We develop a bivariate copula-based statistical model that combines amplitude SAR data and textural information. This model is plugged into a hierarchical Markov random field based on a quadtree structure and on multiscale wavelet features. The contribution of this paper is thus the development of a novel hierarchical classification approach that uses a quadtree model based on the wavelet decomposition and an innovative statistical model. The performance of the developed approach is illustrated on a COSMO-SkyMed image of a urban area