Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

Simple shade plots aid better long-term choices of data pre-treatment in multivariate assemblage studies

Shade plots, simple visual representations of abundance matrices from multivariate species assemblage studies, are shown to be an effective aid in choosing an overall transformation (or other pre-treatment) of quantitative data for long-term use, striking an appropriate balance between dominant and less abundant taxa in ensuing resemblance-based multivariate analyses. Though the exposition is entirely general and applicable to all community studies, detailed illustrations of the comparative power and interpretative possibilities of shade plots are given in the case of two estuarine assemblage studies in south-western Australia: (a) macrobenthos in the upper Swan Estuary over a two-year period covering a highly significant precipitation event for the Perth area; and (b) a wide-scale spatial study of the nearshore fish fauna from five divergent estuaries. The utility of transformations of intermediate severity is again demonstrated and, with greater novelty, the potential importance seen of further mild transformation of all data after differential down-weighting (dispersion weighting) of spatially clumped' or schooled' species. Among the new techniques utilized is a two-way form of the RELATE test, which demonstrates linking of assemblage structure (fish) to continuous environmental variables (water quality), having removed a categorical factor (estuary differences). Re-orderings of sample and species axes in the associated shade plots are seen to provide transparent explanations at the species level for such continuous multivariate patterns.

Living in warmer, more acidic oceans retards physiological recovery from tidal emersion in the velvet swimming crab, Necora puber

The distribution patterns of many species in the intertidal zone are partly determined by their ability to survive and recover from tidal emersion. During emersion, most crustaceans experience gill collapse, impairing gas exchange. Such collapse generates a state of hypoxemia and a hypercapnia-induced respiratory acidosis, leading to hyperlactaemia and metabolic acidosis. However, how such physiological responses to emersion are modified by prior exposure to elevated CO2 and temperature combinations, indicative of future climate change scenarios, is not known. We therefore investigated key physiological responses of velvet swimming crabs, Necora puber, kept for 14 days at one of four pCO(2)/temperature treatments (400 mu atm/10 degrees C, 1000 mu atm/10 degrees C, 400 mu atm/15 degrees C or 1000 mu atm/15 degrees C) to experimental emersion and recovery. Pre-exposure to elevated pCO(2) and temperature increased pre-emersion bicarbonate ion concentrations [HCO3-], increasing resistance to short periods of emersion (90 min). However, there was still a significant acidosis following 180 min emersion in all treatments. The recovery of extracellular acid-base via the removal of extracellular pCO(2) and lactate after emersion was significantly retarded by exposure to both elevated temperature and pCO(2). If elevated environmental pCO(2) and temperature lead to slower recovery after emersion, then some predominantly subtidal species that also inhabit the low to mid shore, such as N. puber, may have a reduced physiological capacity to retain their presence in the low intertidal zone, ultimately affecting their bathymetric range of distribution, as well as the structure and diversity of intertidal assemblages.

Protection of cells from salinity stress by extracellular polymeric substances in diatom biofilms

Diatom biofilms are abundant in the marine environment. It is assumed (but untested) that extracellular polymeric substances(EPS), produced by diatoms, enable cells to cope with fluctuating salinity. To determine the protective role of EPS, Cylindrotheca closterium was grown in xanthan gum at salinities of 35, 50, 70 and 90 ppt. A xanthan matrix significantly increased cell viability (determined by SYTOX-Green), growth rate and population density by up to 300, 2, 300 and 200%, respectively. Diatoms grown in 0.75% w/v xanthan, subjected to acute salinity shock treatments (at salinities 17.5, 50, 70 and 90 ppt) maintained photosynthetic capacity, F_q′/F_m′, within 4% of pre-shock values, whereas F_q′/F_m′ in cells grown without xanthan declined by up to 64% with hypersaline shock. Biofilms that developed in xanthan at standard salinity helped cells to maintain function during salinity shock. These results provide evidence of the benefits of living in an EPS matrix for biofilm diatoms.