Abstract
A rapid atmospheric correction method is proposed to be used for visible and near-infrared satellite sensor images over land. The method is based on a simplified use of a radiative transfer code (RTC), which is used only a priori, to generate Look-Up-Tables (LUTs) of the estimated surface reflectance. A typical scenario and ranges of values for the main atmospheric correction parameters are initially established. Each image pixel is treated as a slight deviation from the reference scenario defined by the vector of the typical values for the parameters. The assumption of the parameter's independence allows the use of one-dimensional LUTs. The method is suitable for near real-time processing or whenever a large number of data are to be handled rapidly. The operator intervention is minimal, and the computation time involved in the correction of a whole image is about 1000 times shorter than the full use of the base RTC. A test is performed with advanced very-high-resolution radiometer (AVHRR) visible and near-infrared data, using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) RTC as the base code. The accuracy of the proposed method was compared with the standard use of the 6S RTC over the same dataset with resulting root mean square errors of 0.0114 and 0.0104 for AVHRR bands 1 and 2 for the estimated surface reflectance, respectively.

Abstract
The use of black white (BW) air photographs for the production of historic land cover maps can be done by image classification, using additional texture features. In this paper we evaluate the importance of a number of parameters in the image classification process based on texture, such as the window size, angle and distance used to produce the texture features, the number of features used, the image quantization level and its spatial resolution. The evaluation was performed using five photographs from the 1950s. The influence of the classification method, the number of classes searched for in the images and the post-processing tasks were also investigated. The effect of each of these parameters for the classification accuracy was evaluated by cross-validation. The selection of the best parameters was performed based on the validation results, and also on the computation load involved for each case and the end user requirements. The final classification results were good (average accuracy of 85.7%, k=0.809) and the method has proven to be useful for the production of historic land cover maps from BW air photographs.

Abstract
The performance of several commercial and experimental software packages (Gotas, StainMaster, ImageTool, StainAnalysis, AgroScan, DropletScan and Spray_imageI and II) that produce indicators of crop spraying quality based on the image processing of water-sensitive papers used as artificial targets were compared against known coverage, droplet size spectra and class size distribution verified through manual counting. A number of artificial targets used to test the software were obtained by controlled spray applications and given droplet density between 14 and 108 drops cm(-2) and a wide range of droplet size spectra. The results showed that artificial targets coupled with an appropriate image system can be an accurate technique to compute spray parameters. The between-methods differences were 6.7% for droplet density, 11.5% for volume median diameter, <3% for coverage (%) and <3% coverage density. For the 16 droplet class size distribution tested the between-methods differences were all <15%. However, most of the image analysis systems were not effective in accurately measuring coverage density when coverage rate is greater than about 17%. The Spray_imageII software estimated the coverage density with a mean absolute error of 2% and the absolute error is below 10%, even with about 43% of coverage rate. This software, when compared to the other programmes tested, provided the best accuracy for coverage and droplet size spectrum as well as for droplet class size distribution.

Abstract
Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) segmentation, (ii) feature extraction and selection, (iii) lesion classification. This paper evaluates the potential of an alternative approach based on the Menzies method presence of 1 or more of 6 color classes, indicating that the lesion should be considered a potential melanoma. This method does not require stages (i) and (ii) - lesion segmentation and feature extraction. The Jeffries-Matusita and Transformed Divergence metrics were used to evaluate the color class separability. The preliminary results presented in this paper suggest that a system based on the Menzies method could provide valuable information for automatic dermoscopic image analysis.

Abstract
This paper presents a method (GEIAS) for the automatic processing of digital images obtained from Gel Electrophoresis. The performance of GEMS was tested using 12 images, obtained from 4 gels with 3 different exposures with a total of 1082 bands, comparing the results provided by GEIAS and 3 other software tools. The GEIAS is able to fully automatically detect; DNA lanes while the other 3 software tools tested can only do this in a semi-automatic or manual way. For the correct location of DNA bands, GEIAS required a manual correction of the location in 10.0% of the bands, and the other software tools 13.0%, 15.0% and 25.4%. The average error in the estimation of molecular weight was tested using a total of 5443 bands in 12 image using 672 reference/observed lane pairs. The average error was found to be 9.2% for GEIAS and 11.2%, 14.4% and 13.1% for the other software tools tested.

Abstract
Polar orbital satellites with low spatial resolution sensors, such as the AVHRR, provide global coverage with a short repetition period. The data is directly transmitted to ground stations, and can be distributed immediately after data acquisition. Near real time applications can be implemented if the adequate processing tools are available. One task usually needed is the geometric correction of image data. Automatic methods, based on satellite orbital parameters, can in some cases provide satisfactory results. However, the identification of Ground Control Points (GCPs) is generally required in order to achieve registration errors below the pixel size. A fully automatic method for the geometric registration of AVHRR data is proposed here. The method comprises four stages: (i) an initial image transformation based on orbital parameters, (ii) image segmentation of this image into 3 main classes (land, water, cloud) and 9 additional classes of mixed water and land at various levels, (iii) automatic GCP collection by image matching, (iv) final image production combining both orbital and GCP information. The method was tested on ten images of the Iberian Peninsula, and proved effective in accurately geo-referencing sub-sections of an AVHRR scene of medium dimension in a few minutes.