Image Analysis of Soil Micromorphology: Feature Extraction, Segmentation, and Quality Inference
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Image Analysis of Soil Micromorphology: Feature Extraction, Segmentation, and Quality Inference Petros Maragos School of Electrical & Computer Engineering, National Technical University of Athens, Athens 15773, Greece Email: [email protected]
Anastasia Sofou School of Electrical & Computer Engineering, National Technical University of Athens, Athens 15773, Greece Email: [email protected]
Giorgos B. Stamou School of Electrical & Computer Engineering, National Technical University of Athens, Athens 15773, Greece Email: [email protected]
Vassilis Tzouvaras School of Electrical & Computer Engineering, National Technical University of Athens, Athens 15773, Greece Email: [email protected]
Efimia Papatheodorou Department of Biology, Ecology Division, Aristotle University of Thessaloniki, Thessaloniki 54006, Greece Email: [email protected]
George P. Stamou Department of Biology, Ecology Division, Aristotle University of Thessaloniki, Thessaloniki 54006, Greece Email: [email protected] Received 6 February 2003; Revised 15 December 2003 We present an automated system that we have developed for estimation of the bioecological quality of soils using various image analysis methodologies. Its goal is to analyze soilsection images, extract features related to their micromorphology, and relate the visual features to various degrees of soil fertility inferred from biochemical characteristics of the soil. The image methodologies used range from low-level image processing tasks, such as nonlinear enhancement, multiscale analysis, geometric feature detection, and size distributions, to object-oriented analysis, such as segmentation, region texture, and shape analysis. Keywords and phrases: soilsection image analysis, geometric feature extraction, morphological segmentation, multiscale texture analysis, neurofuzzy quality inference.
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INTRODUCTION
The goal of this research work is the automated estimation of the bioecological quality of soils using image processing and computer vision techniques. Estimating the soil quality with the traditional biochemical methods, and more specifically estimating those elements that are essential components for the soil fertility, is a diļ¬cult, time-consuming, and expensive process, which is, however, necessary for selecting and applying any management practice to land ecosys-
tems. Our approach has been the development of an automated system that will recognize the characteristics relevant to the soil quality by computer processing of soilsection images and extraction of suitable visual features. Its final goals are double-fold: (1) quantification of the micromorphology of the soil via analysis of soilsection images and (2) correspondence of the extracted visual information with the classification of soil into various fertility degrees inferred from measurements performed biochemically on the soil samples. The overall system is shown in Figure 1.
Soil Image Analysis
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Chemical analysis Soil (map image)
Soil sampling
Digital image acquisition system (digital camera, sca
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