Dominant run-length method for image classification

In this paper, we develop a new run-length texture feature extraction algorithm that significantly improves image classification accuracy over traditional techniques. By directly using part or all of the run-length matrix as a feature vector, much of the texture information is preserved. This approach is made possible by the introduction of a new multi-level dominant eigenvector estimation algorithm. It reduces the computational complexity of the Karhunen-Loeve Transform by several orders of magnitude. Combined with the Bhattacharya distance measure, they form an efficient feature selection algorithm. The advantage of this approach is demonstrated experimentally by the classification of two independent texture data sets. Perfect classification is achieved on the first data set of eight Brodatz textures. The 97% classification accuracy on the second data set of sixteen Vistex images further confirms the effectiveness of the algorithm. Based on the observation that most texture information is contained in the first few columns of the run-length matrix, especially in the first column, we develop a new fast, parallel run-length matrix computation scheme. Comparisons with the co-occurrence and wavelet methods demonstrate that the run-length matrices contain great discriminatory information and that a method of extracting such information is of paramount importance to successful classification.Funding was provided by the Office of Naval Research through Contract No. N00014-93-1-0602

Use of Wavelet Transform in Digital Aerial Images

The Wavelet transform primarily performs a filter function on images. There are two types of wavelet transform first continuous wavelet Transform and second is discrete wavelet Transform. We will used Discrete wavelet Transform. The paper represents a new computational scheme Based on multiresolution decomposition for extracting the features of interest from the Digital Aerial Images by suppressing noise. In this paper, first of all preprocessing on the aerial images is done. These are converting to grayscale if necessary, applying wavelets transform to improve the quality of image DOI: 10.17762/ijritcc2321-8169.15028

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Seasonal and interannual variability of surface chlorophyll-a and sea surface temperature in the Delgoa Bight, southern Mozambique

Multi satellite data for surface chlorophyll‐a (Chl‐a), sea surface temperature (SST), sea surface wind (SSW) and sea level anomalies (SLA) have been obtained and analysed over the Delagoa Bight (24‐28°S, 32‐36°E), southern Mozambique for the period 2003‐2012 at monthly time scales. Both descriptive and quantitative analysis using wavelets have been used to obtain a better understanding of the nature of the interannual, seasonal and intra- seasonal variability of the data. Strong seasonal structure and interannual modulation were observed in the area averaged Chl‐a concentration and SST. The lowest maximum in monthly Chl-a was in December (0.127 mg.m--‐3) and the highest in August (0.541 mg.m‐3). The lowest maximum in monthly SST was in August (21.8°C) and the maximum in February (27.9°C). The Chl‐a and SST were strongly anti-correlated and both exhibited a well- defined seasonal cycle, contrasting with the SSW and SLA. The daily observations of temperature at 17 meters depth, from the northern Delagoa Bight at Ponta Zavora (24.48°S- 35.24°E) for the period 2006‐2011, have confirmed a seasonal signal with amplitude of about 6.5°C. Cool coastal water events were found mostly in summer and spring, with maximum amplitude of 6°C. Further analysis of this daily data did not reveal the timing of such events to be regular

Extraction of coherent structures in a rotating turbulent flow experiment

The discrete wavelet packet transform (DWPT) and discrete wavelet transform (DWT) are used to extract and study the dynamics of coherent structures in a turbulent rotating fluid. Three-dimensional (3D) turbulence is generated by strong pumping through tubes at the bottom of a rotating tank (48.4 cm high, 39.4 cm diameter). This flow evolves toward two-dimensional (2D) turbulence with increasing height in the tank. Particle Image Velocimetry (PIV) measurements on the quasi-2D flow reveal many long-lived coherent vortices with a wide range of sizes. The vorticity fields exhibit vortex birth, merger, scattering, and destruction. We separate the flow into a low-entropy ``coherent'' and a high-entropy ``incoherent'' component by thresholding the coefficients of the DWPT and DWT of the vorticity fields. Similar thresholdings using the Fourier transform and JPEG compression together with the Okubo-Weiss criterion are also tested for comparison. We find that the DWPT and DWT yield similar results and are much more efficient at representing the total flow than a Fourier-based method. Only about 3% of the large-amplitude coefficients of the DWPT and DWT are necessary to represent the coherent component and preserve the vorticity probability density function, transport properties, and spatial and temporal correlations. The remaining small amplitude coefficients represent the incoherent component, which has near Gaussian vorticity PDF, contains no coherent structures, rapidly loses correlation in time, and does not contribute significantly to the transport properties of the flow. This suggests that one can describe and simulate such turbulent flow using a relatively small number of wavelet or wavelet packet modes.Comment: experimental work aprox 17 pages, 11 figures, accepted to appear in PRE, last few figures appear at the end. clarifications, added references, fixed typo

MEC: A Mesoscale events classifier for oceanographic imagery

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT’s Metop and NASA’s Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.info:eu-repo/semantics/publishedVersio

Detecting anomalies in remotely sensed hyperspectral signatures via wavelet transforms

An automated subpixel target detection system has been designed and tested for use with remotely sensed hyperspectral images. A database of hyperspectral signatures was created to test the system using a variety of Gaussian shaped targets. The signal-to-noise ratio of the targets varied from -95dB to -50dB. The system utilizes a wavelet-based method (discrete wavelet transform) to extract an energy feature vector from each input pixel signature. The dimensionality of the feature vector is reduced to a one-dimensional feature scalar through the process of linear discriminant analysis. Signature classification is determined by nearest mean criterion that is used to assign each input signature to one of two classes, no target present or target present. Classification accuracy ranged from nearly 60% with target SNR at -95dB without any a priori knowledge of the target, to 100% with target SNR at -50dB and a priori knowledge about the location of the target within the spectral bands of the signature