A new technique for fast pattern recognition using normalized grey-scale correlation (NGC) is described. While NGC has traditionally been slow due to computational intensity issues, the introduction of both a pyramid structure and a local estimate of the correlation surjace gradient allows for recognition in 10-50 ms using modest microcomputer hardware. The algorithm is designed to analyze the ...

--Thresholding, the problem of pixel classification is attempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated in the presence of distinct region proportions. The hard k means and fuzzy c means algorithms have been found useful w...

Stereo correspondence algorithm depends on matching costs in order to compute the similarity of the image locations. The insensitivity of various cost functions for stereo algorithm is evaluated with respect to radiometric variations of the given input image. We consider both the window based variants as well as pixel based variants like the absolute difference, the sampling insensitivity absol...

Many researchers adopt Local Binary Pattern for pattern analysis. However, the long histogram created by Local Binary Pattern is not suitable for large-scale facial database. This paper presents a simple facial pattern descriptor for facial expression recognition. Local pattern is computed based on local gradient flow from one side to another side through the center pixel in a 3x3 pixels region...

Earth observation (EO) data are of great value for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of available remote sensing data, innovative and reliable landslide (change) detection methods are needed. To overcome the limitations of pixel-based methods object-based image a...

Reconstructing the missing data for cloud/shadow-covered optical satellite images has great significance enhancing availability and multi-temporal analysis. In this study, we proposed a deep-learning-based method reconstruction time-series Landsat images. Central to is combined use of autoencoder, long-short-term memory (AE-LSTM)-based similar pixel clustering backward LSTM-based prediction. Fi...

Numerous studies have used satellite images for mapping urban land cover and land use along with modelling green spaces and surface impermeability. Recently, monitoring the percentage of sealed soils in urban environments is of great interest as a key indicator of sustainable landuse. The aim of this research is to identify an appropriate methodology to classify sealed soil and green space surf...

Burn severity classifications derived from multitemporal Landsat Thematic Mapper images and the Normalised Burn Ratio (NBR) are commonly used to assess the post-fire ecological effects of wildfires. Ongoing efforts to retrospectively map historical burn severity require defensible, objectivemethods of classifying continuous differenced Normalised Burn Ratio (dNBR) data where field data are ofte...

This document describes a capstone learning exercise designed for undergraduates enrolled in an introductory geospatial tools course. The overarching theme of the exercise, Colorado 14ers, Pixel by Pixel, is mountain geography. While immersed in a digital mountainous landscape, students explore topics of geomorphology and geomorphometry and discover how it is possible for a summit that rises we...