Pixel size for the imagery was 0.6m. The imagery was orthorectified using a bilinear interpolation resampling method, and meets the National Mapping Accuracy Standards for scale mapping of 1:3,000 (3-meter accuracy with 90% confidence). LIDAR data used in this study were acquired in March 2002. Both the first and last vertical returns were recorded for each laser pulse, with an average point spacing of approximately 1.3m. A surface cover height model with 1-m spatial resolution was derived from the LIDAR data, which was used to aid in land cover classification.Property parcel boundaries and building footprints datasets were obtained in digital format from Baltimore City and Baltimore County municipal governments, and were used to both facilitate object segmentation and obtain greater classification accuracy. The parcel boundaries had a high degree of spatial accuracy when compared with 1:3,000 scale 0.5m aerial imagery. A limited assessment was conducted to compare the building footprints to the Emerge image data. The comparison indicates that the building footprints agree spatially with the Emerge imagery, but a small proportion of building footprints have not yet been digitized by municipal data providers in the study area.3.2. Object-based classificationAn object-based approach was used to conduct the classification separately on the data collected for the two years. Five land cover classes were used: 1) buildings, 2) pavement, 3) coarse selleck 17-AAG textured vegetation (trees and shrubs), 4) fine textured vegetation (herbaceous vegetation and grasses), and 5) bare soil [26]. Here, we briefly describe the classification processes. A more detailed account is given in Zhou and Troy [19].We first segmented the image
Evanescent field absorption (EFA) has been widely employed by some researchers for sensing of chemical and biological parameters over the past two decades [1, 2]. Typical applications include sensing of chemical species such that pH [3,4], concentration [5], humidity [6], gas [7], combustible liquids [8] and as biosensors [9].EFA sensing mechanism utilized by fiber optic sensors is based on the absorption of the light carried by the evanescent field that coexists in the fiber cladding. In EFA fiber sensors the core is surrounded by an absorptive cladding that consists of a liquid [10, 11] or a sol-gel material [3, 12] that include an absorbing dye, since most chemical materials are not able to absorb the optical power directly. The effect to be sensed can be related to the power change because absorbing dye causes attenuation in the optical power.In the last decade, ANNs became popular because they have ability to learn, fast real-time operation and ease of implementation features [13-15].