Quantification of soil mapping by digital analysis of LANDSAT data

Soil survey mapping units are designed such that the dominant soil represents the major proportion of the unit. At times, soil mapping delineations do not adequately represent conditions as stated in the mapping unit descriptions. Digital analysis of LANDSAT multispectral scanner (MSS) data provides a means of accurately describing and quantifying soil mapping unit composition. Digital analysis of LANDSAT MSS data collected on 9 June 1973 was used to prepare a spectral soil map for a 430-hectare area in Clinton County, Indiana. Fifteen spectral classes were defined, representing 12 soil and 3 vegetation classes. The 12 soil classes were grouped into 4 moisture regimes based upon their spectral responses; the 3 vegetation classes were grouped into one all-inclusive class

Sonar Signal Enhancement Using Fractional Fourier Transform

In this paper we present an approach for signal enhancement of sonar signals. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), as well as VSS synthetic data. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm. The proposed technique will allow efficient determination of seafloor bottom characteristics and bottom type using the reverberation signal. A study is carried out to compare the performance of the presented method with conventional methods. Results are shown and future work and recommendations are presented

Acoustic Seabed Classification using Fractional Fourier Transform

In this paper we present a time-frequency approach for acoustic seabed classification. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), one of the five sonar systems in the AN/AQS-20. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm in order to determine the impulse response of the sediment. The Fractional Fourier transform requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Singular Value Decomposition and statistical properties of the Wigner and Choi-Williams distributions of the bottom impulse response are employed as features which are, in turn, used for classification. The Wigner distribution can be thought of as a signal energy distribution in joint time-frequency domain. Results of our study show that the proposed technique allows for accurate sediment classification of seafloor bottom data. Experimental results are shown and suggestions for future work are provided

Time-Frequency Transform Techniques for Seabed and Buried Target Classification

An approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification and underwater buried target classification is presented in this paper. Work reported for sediment classification is based on sonar data collected by one of the AN/AQS-20’s sonars. Synthetic data, simulating data acquired by parametric sonar, is employed for target classification. The technique is based on the Fractional Fourier Transform (FrFT), which is better suited for sonar applications because FrFT uses linear chirps as basis functions. In the first stage of the algorithm, FrFT requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Then, the magnitude of the Fractional Fourier transform for optimal order applied to the backscattered signal is computed in order to approximate the magnitude of the bottom impulse response. Joint time-frequency representations of the signal offer the possibility to determine the timefrequency configuration of the signal as its characteristic features for classification purposes. The classification is based on singular value decomposition of the time-frequency distributions applied to the impulse response. A set of the largest singular values provides the discriminant features in a reduced dimensional space. Various discriminant functions are employed and the performance of the classifiers is evaluated. Of particular interest for underwater under-sediment classification applications are long targets such as cables of various diameters, which need to be identified as different from other strong reflectors or point targets. Synthetic test data are used to exemplify and evaluate the proposed technique for target classification. The synthetic data simulates the impulse response of cylindrical targets buried in the seafloor sediments. Results are presented that illustrate the processing procedure. An important characteristic of this method is that good classification accuracy of an unknown target is achieved having only the response of a known target in the free field. The algorithm shows an accurate way to classify buried objects under various scenarios, with high probability of correct classification

Automated Grain Yield Behavior Classification

A method for classifying grain stress evolution behaviors using unsupervised learning techniques is presented. The method is applied to analyze grain stress histories measured in-situ using high-energy X-ray diffraction microscopy (HEDM) from the aluminum-lithium alloy Al-Li 2099 at the elastic-plastic transition (yield). The unsupervised learning process automatically classified the grain stress histories into four groups: major softening, no work-hardening or softening, moderate work-hardening, and major work-hardening. The orientation and spatial dependence of these four groups are discussed. In addition, the generality of the classification process to other samples is explored