Changing land-cover in the tropics is a central issue in global change research. This dissertation used Landsat-TM data to examine processes of land-use and land-cover changes for a lowland tropical site in Sarapiqui,...
Changing land-cover in the tropics is a central issue in global change research. This dissertation used Landsat-TM data to examine processes of land-use and land-cover changes for a lowland tropical site in Sarapiqui, Costa Rica. Performances of selected image-processing methods to detect and identify land-cover changes were evaluated. A land-cover time-series from 1960 to 1996 for the site was generated using maps derived from aerial photographs and Landsat-TM classifications. Changes in land-cover from 1986 to 1996 were evaluated using standard landscape indices, and interpreted in terms of their historical context. Dominant changes in the site during this decade included the breakup of extensive cattle ranches for large-scale plantation enterprises and small-scale farming. Colonization processes, improvements in access, and changes in export markets were identified as the major driving forces of change. Evaluation of change-detection methods revealed that postclassification comparison performed significantly better than image differencing algorithms. image differencing using mid- infrared bonds performed the best of the differencing algorithms tested. Selection of a suitable change-detection method can be aided through examination of the individual bond statistics for the specific area and problem in question. The univariate bond differencing technique has potential for identification of 'hot spots' of change using Landsat-TM data. Spatial pattern-recognition techniques to characterize complexity of Landsat-TM data were evaluated. Fractal dimension calculated using the triangular prism surface area method, and Moran's I index of spatial autocorrelation, clearly distinguished different land-cover types. Shannon's diversity index and the contagion metric were not found to be useful in characterizing the images. The use of fractal dimension, in conjunction with standard non-spatial descriptive band statistics, are seen as having great potential in characterizing unc
Mathematical morphology coupled with creation of a time stack image and principal oscillation pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. ...
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Mathematical morphology coupled with creation of a time stack image and principal oscillation pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. The data, produced by a simulator, consisted of 60 images, each 256/spl times/256 pixels, separated by 1 second in time. The mathematical depth results are compared with those derived using principal oscillation pattern analysis by which three significant complex pairs of patterns are found with their corresponding characteristic times: e-folding times and periods. From these results and by using the classical hydrodynamic theory of gravity waves, water depths are determined. Both analyses yield small errors for the simulated data, indicating both methods should perform reliably for real data.
We describe focal plane image processors which filter images in space by filters which are similar to even and odd Gabor filters. All processing is done using analog circuits fabricated on the same die as the photosen...
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We describe focal plane image processors which filter images in space by filters which are similar to even and odd Gabor filters. All processing is done using analog circuits fabricated on the same die as the photosensors allowing combined sensing and processing at rates as high as 5000 frames per second. Both 1D and 2D sensors have been designed, fabricated and tested. In the 2D sensor the tuned orientation can be steered and the filter response scaled by adjusting external bias voltages controlling parameters such as the gains of the analog processing circuits. Because both even and odd Gabor-type filter outputs are calculated, sensors can be used in Gabor phase-based algorithms. As a simple example, we have embedded a 1D sensor on a mobile robot platform to perform image fixation.
We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multi...
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We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed and a discussion is given as to why our algorithm is optimal and outperforms them. A variety of experimental results are presented.
This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed s...
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This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed system are described, namely the filtering and statistical modules, automatic feature vector optimization module and the classification module using clustering and fuzzy clustering methods. This texture classification system can easily be adapted for other tasks, including tasks in the field of medical imaging, remotesensing and quality control.
The field of remotesensing and sensor technology has undergone tremendous development in the past decades. Sensor technologies of all kinds such as electro-optics, acoustic, active/passive UV to LWIR, ground-penetrat...
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The field of remotesensing and sensor technology has undergone tremendous development in the past decades. Sensor technologies of all kinds such as electro-optics, acoustic, active/passive UV to LWIR, ground-penetrating radar, passive mm wavelength, X-ray tomography, neutron activation imaging, multispectral, hyper-spectral, and ultra-spectral imaging, provide valuable images that normal CCD cameras cannot offer. By combining algorithms and images taken by sensors at different ranges of the electromagnetic spectrum, we are able to extract valuable images automatically. By using multi-spectral images and processing them with neural network computing, our "third eye" team is able to extract human face features from those images. We present an application for detecting human facial parts, images taken by different imaging systems and sensors, and the current status of imageprocessing applications.
Unsupervised classification algorithms are techniques to extract information from remotesensingimagery based on machine calculation without prior knowledge of labeled samples. Most of current unsupervised algorithms...
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ISBN:
(纸本)0819434663
Unsupervised classification algorithms are techniques to extract information from remotesensingimagery based on machine calculation without prior knowledge of labeled samples. Most of current unsupervised algorithms only use the spectral response as information. The clustering algorithms that takes into consideration the spatial information have a trade off between being accurate and time consuming, or being fast and losing relevant details in the spatial mapping. This paper will present an unsupervised classification system developed to extract information from multispectral and hyperspectral data as well, considering the spectral response, hyperdimensional data characteristics, and the spatial context of the pixel that will be classified. This algorithm constructs local spatial neighborhoods in order to measure their degrees of homogeneity. It resembles the supervised version of the ECHO classifier. An advantage of this mechanism is that the mathematical developments to estimate the degrees of homogeneity enable implementations based on statistical patternrecognition. This clustering algorithm is fast and its results have shown superiority in recognizing objects in multispectral and hyperspectral data over other known mechanism.
We have proposed a novel type of image sensor named artificial retina LSIs (AR LSls) which combine both functions of imagesensing and imageprocessing, similar to our human eyes. The operation principle is based on t...
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We have proposed a novel type of image sensor named artificial retina LSIs (AR LSls) which combine both functions of imagesensing and imageprocessing, similar to our human eyes. The operation principle is based on the optoelectronic vector/matrix multiplier, where the input image is corresponding to the matrix, and the electric control signal to the vector. The functions of the imageprocessing include simple image capturing, edge extraction, 2D to 1D image projection, random access, resolution control and so on. These functions can be selected just by changing the pattern of the electric control signal. The optoelectronic multiplier operates in a semi-parallel and an analog mode. We have developed four kinds of AR LSls based on the CMOS technology.
Instead of piecemeal approaches for detecting specific patterns in the SODAR echograms, an integrated modular approach 'towards' automatic interpretation of the ABL structure patterns, as depicted in the SODAR...
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Instead of piecemeal approaches for detecting specific patterns in the SODAR echograms, an integrated modular approach 'towards' automatic interpretation of the ABL structure patterns, as depicted in the SODAR facsimile records, is presented. Here we propose a unified approach where the user is at liberty to select a wide range of imageprocessing and the patternrecognition techniques required to extract remotely-probed meteorological information from the closest geometric representation of the SODAR pattern boundary. The ultimate goal, part of which is already implemented and reported here, is to generate 'expert-like' interpretation of SODAR echograms.
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