Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts

Context-aware Assessment Using QR-codes

In this paper we present the implementation of a general mechanism to deliver tests based on mobile devices and matrix codes. The system is an extension of Siette, and has not been specifically developed for any subject matter. To evaluate the performance of the system and show some of its capabilities, we have developed a test for a second-year college course on Botany at the School of Forestry Engineering. Students were equipped with iPads and took an outdoor test on plant species identification. All students were able to take and complete the test in a reasonable time. Opinions expressed anonymously by the students in a survey about the usability of the system and the usefulness of the test were very favorable. We think that the application presented in this paper can broaden the applicability of automatic assessment techniques.The presentation of this work has been co-founded by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

Identification and measurement of shrub type vegetation on large scale aerial photography

Important range-shrub species were identified at acceptable levels of accuracy on large-scale 70 mm color and color infrared aerial photographs. Identification of individual shrubs was significantly higher, however, on color infrared. Photoscales smaller than 1:2400 had limited value except for mature individuals of relatively tall species, and then only if crown margins did not overlap and sharp contrast was evident between the species and background. Larger scale photos were required for low-growing species in dense stands. The crown cover for individual species was estimated from the aerial photos either with a measuring magnifier or a projected-scale micrometer. These crown cover measurements provide techniques for earth-resource analyses when used in conjunction with space and high-altitude remotely procured photos

Use of LANDSAT data for automatic classification and area estimation of sugarcane plantation in Sao Paulo state, Brazil

Ten segments of the size 20 x 10 km were aerially photographed and used as training areas for automatic classifications. The study areas was covered by four LANDSAT paths: 235, 236, 237, and 238. The percentages of overall correct classification for these paths range from 79.56 percent for path 238 to 95.59 percent for path 237

Remote sensing for detection of soil limitations in agricultural areas Quarterly report, 1 Apr. - 1 Jul. 1970

Remote sensing for detecting soil limitations in agricultural area

Automatic interpretation of MSS-LANDSAT data applied to coal refuse site studies in southern Santa Catarina State, Brazil

The coal mining district in southeastern Santa Catarina State is considered one of the most polluted areas of Brazil. The author has identified significant preliminary results on the application of MSS-LANDSAT digital data to monitor the coal refuse areas and its environmental consequences in this region