Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms?

tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier approximation and a recursive median sieve) could be automatically applied to time series classification problems as a preprocessing step to improve the performance of three benchmark classifiers (1-Nearest Neighbour with Euclidean and Dynamic Time Warping distances, and Rotation Forest). We found no significant improvement over unsmoothed data even when we set the smoothing parameter through cross validation. We are not claiming smoothing has no worth. It has an important role in exploratory analysis and helps with specific classification problems where domain knowledge can be exploited. What we observe is that the automatic application does not help and that we cannot explain the improvement of other time series classification algorithms over the baseline classifiers simply as a function of the absence of smoothing

Multiscale Analysis for Characterization of Remotely Sensed Images.

In this study we addressed fundamental characteristics of image analysis in remote sensing, enumerated unavoidable problems in spectral analysis, and highlighted the spatial structure and features that increase information amount and measurement accuracy. We addressed the relationship between scale and spatial structure and the difficulties in characterizing them in complex remotely sensed images. We suggested that it is necessary to employ multiscale analysis techniques for analyzing and extracting information from remotely sensed images. We developed a multiscale characterization software system based on an existing software called ICAMS (Image Characterization And Modeling System), and applied the system to various test data sets including both simulated and real remote sensing data in order to evaluate the performance of these methods. In particular, we analyzed the fractal and wavelet methods. For the fractal methods, the results from using a set of simulated surfaces suggested that the triangular prism surface area method was the best technique for estimating the fractal dimension of remote sensing images. Through examining Landsat TM images of four different land covers, we found that fractal dimension and energy signatures derived from wavelets can measure some interesting aspects of the spatial content of remote sensing data, such as spatial complexity, spatial frequency, and textural orientation. Forest areas displayed the highest fractal dimension values, followed by coastal, urban, and agriculture respectively. However, fractal dimension by itself is insufficient for accurate classification of TM images. Wavelet analysis is more accurate for characterizing spatial structures. A longer wavelet was shown to be more accurate in the representation and discrimination of land-cover classes than a similar function of shorter length, and the combination of energy signatures from multiple decomposition levels and multispectral bands led to better characterization results than a single resolution and single band decomposition. Significant improvements in classification accuracy were achieved by using fractal dimensions in conjunction with the energy signature. This study has shown that multiscale analysis techniques are very useful to complement spectral classification techniques to extract information from remotely sensed images

Colour morphological sieves for scale-space image processing

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Scale-space from nonlinear filters

Decomposition by extrema is put into the context of linear vision systems and scale-space. It is proved that discrete one-dimensional, M- and N-sieves neither introduce new edges as the scale increases nor create new extrema. They share this property with diffusion based filters. They are robust and preserve edges of large scale feature