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.

Quantification of oxygenated volatile organic compounds in seawater by membrane inlet-proton transfer reaction/mass spectrometry

The role of the ocean in the cycling of oxygenated volatile organic compounds (OVOCs) remains largely unanswered due to a paucity of datasets. We describe the method development of a membrane inlet-proton transfer reaction/mass spectrometer (MI-PTR/MS) as an efficient method of analysing methanol, acetaldehyde and acetone in seawater. Validation of the technique with water standards shows that the optimised responses are linear and reproducible. Limits of detection are 27 nM for methanol, 0.7 nM for acetaldehyde and 0.3 nM for acetone. Acetone and acetaldehyde concentrations generated by MI-PTR/MS are compared to a second, independent method based on purge and trap-gas chromatography/flame ionisation detection (P&T-GC/FID) and show excellent agreement. Chromatographic separation of isomeric species acetone and propanal permits correction to mass 59 signal generated by the PTR/MS and overcomes a known uncertainty in reporting acetone concentrations via mass spectrometry. A third bioassay technique using radiolabelled acetone further supported the result generated by this method. We present the development and optimisation of the MI-PTR/MS technique as a reliable and convenient tool for analysing seawater samples for these trace gases. We compare this method with other analytical techniques and discuss its potential use in improving the current understanding of the cycling of oceanic OVOCs.

Joint distributions of waves and rain

The transfer of gases between the atmosphere and ocean is affected by a number of processes, of which wave action and rainfall are two of potential significance. Efforts have been made to quantify separately their contributions; however such assessments neglect the interaction of these phenomena. Here we look at the correlation statistics of waves and rain to note which regions display a strong association between rainfall and the local sea state. The conditional probability of rain varies from ~0.5% to ~15%, with most of the equatorial belt (which contains the ITCZ) showing a greater likelihood of rain at the lowest sea states. In contrast the occurrence of rain is independent of wave height in the Southern Ocean. The 1997/98 El Niño enhances the frequency of rain in some Pacific regions, with this change showing some association with wave conditions.