This study assesses machine learning methods for the classification of an intertidal reef using RGB and multispectral imagery acquired by an unmanned aerial vehicle (UAV). After the photogrammetric processing of the acquired data an orthophoto mosaic was generated, from the RGB imagery, and the reflectance of four bands (green, red, red edge and near infrared) from the multispectral data. Four machine learning classifiers were evaluated: support vector machines (SVM), artificial neural networks (ANN) naive Bayes (NB) and random forests (RF). The data was classified into four classes: sand; rock, barnacles, limpets; mussels, rock; and algae mixed. The classifiers were trained with RGB and with multispectral data. The pixel-based classification results demonstrated that when using multispectral data all classifiers overcame the performance achieved when using RGB data. NB classifier performed better in discriminating all classes and detecting submerged seaweeds. Such techniques present a valuable tool for accurately map the coastal zone.

Automatic image registration (AIR) is still a present challenge for the remote sensing community. Although a wide variety of AIR methods have been proposed in the last few years, there are several drawbacks which avoid their common use in practice. The recently proposed scale invariant feature transform (SIFT) approach has already revealed to be a powerful tool for the obtention of tie points in general image processing tasks, but it has a limited performance when directly applied to remote sensing images. In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal. This combination allows for an accurate obtention of tie points for a pair of remote sensing images, being a powerful scheme for AIR. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology, comprising medium and high spatial resolution images, and single-band, multispectral, and hyperspectral images. A set of measures which allow for an objective evaluation of the geometric correction process quality has been used. The proposed methodology allows for a fully automatic registration of pairs of remote sensing images, leading to a subpixel accuracy for the whole considered data set. Furthermore, it is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination.

Automatic image registration is still an actual challenge in several fields. Although several methods for automatic image registration have been proposed in the last few years, it is still far from a broad use in several applications, such as in remote sensing. In this paper, a method for automatic image registration through histogram-based image segmentation (HAIRIS) is proposed. This new approach mainly consists in combining several segmentations of the pair of images to be registered, according to a relaxation parameter on the histogram modes delineation (which itself is a new approach), followed by a consistent characterization of the extracted objects--through the objects area, ratio between the axis of the adjust ellipse, perimeter and fractal dimension--and a robust statistical based procedure for objects matching. The application of the proposed methodology is illustrated to simulated rotation and translation. The first dataset consists in a photograph and a rotated and shifted version of the same photograph, with different levels of added noise. It was also applied to a pair of satellite images with different spectral content and simulated translation, and to real remote sensing examples comprising different viewing angles, different acquisition dates and different sensors. An accuracy below 1° for rotation and at the subpixel level for translation were obtained, for the most part of the considered situations. HAIRIS allows for the registration of pairs of images (multitemporal and multisensor) with differences in rotation and translation, with small differences in the spectral content, leading to a subpixel accuracy.

PRISM is a three-line scanner, on board of ALOS satellite, fully dedicated to the acquisition of high resolution panchromatic stereo imagery, for the generation of global terrain elevation data. Common processes of DSM generation required accurate ground control data, which may not be available in many areas were these imagery may be more useful. This paper presents a fully automatic method for ALOS-PRISM image orientation and DSM extraction without using conventional ground control points. It is based on the extraction of a relative DSM, which is adjusted by surface matching to the SRTM-DSM. This method proved to be accurate enough for the requirements of medium map scale production, such as 1:25.000.

The geometric correction process is a crucial step in remote sensing applications. This process is frequently manually performed-which is a laborious task in many situations-as automatic image registration methods are still far from being broadly applied. One of the reasons that justify the absence of a broad application of automatic image registration methods is the lack of measures for an objective and automated analysis of the image registration process quality. The root mean square (RMS) of the residuals is the only quantitative evaluation which is generally used in this process, with the final validation of the geometric correction process being a qualitative analysis. Therefore, in both ldquohumanrdquo and automatic image registration processes, an objective evaluation of its quality is required. In this letter, we propose several measures for an objective evaluation of the geometric correction process, as a complement to the traditional RMS of the residuals and visual inspection. Two scenarios of control point distribution and the most common residual distributions were considered. With the proposed measures, we intend to cover the most common qualitative analysis aspects. This has particular importance under the scope of automatic image registration methods, where an automatic evaluation of the results is also required.

Spatial and temporal variation of river plumes can be studied by remote sensing. The main objectives of this letter were to model the Douro River Plume (DRP) size based on image segmentation techniques and to relate it to different parameters such as water volume, last available plume, tide height, and wind speed. Twenty-one MEdium Resolution Imaging Spectrometer (MERIS) scenes of the study area were considered, covering 20 months from 2003 to 2005. Two different segmentation techniques were applied (watershed and region based) to the MERIS scenes in order to estimate the DRP size. Two models were developed in order to relate the DRP size with the water volume, last available plume, tide height, and wind speed. In the first model, the DRP size was modeled using the discharged water volume as input. In the second model, the DRP size was retrieved as a function of the water volume, last available plume, tide height, and wind speed. The last available plume-a factor of crucial importance-was weighted using a forgetting factor, function of the time difference between two consecutive MERIS scenes. A significant correlation was found between DRP dimension and water volume (r = 0.66) during wet season (i.e., excluding dry summer period), because the plume derived from MERIS imagery in the region under study represents river Douro plume only when the river flow exceeds a certain threshold. Despite some particular points, the second model was able to model the plume size obtained through image segmentation, with a mean percentage variation of 34.8% for region-based segmentation method. Segmentation of MERIS data have been shown to be a valid method for modeling the DRP size. Furthermore, DRP size was found to be proportional to water volume, excluding the summer period.

Images acquired by linear array sensors on board of satellites, such as SPOT, are oriented, for mapping purposes, by rigorous sensor models. These models use a set of orbital and attitude parameters that represent the exterior orientation of an image. Rigorous values of the exterior orientation parameters are determined with ground control points (GCPs). In this article we present an alternative methodology of linear array image orientation. The sensor model is set up with approximate parameters provided with image data. The improvement of image orientation is done by applying corrections in image space. The paper justifies the conditions in which the method is applicable. Several experiments of the method were carried out with images of a mountainous region in Portugal. Initial orientation parameters, provided with images, had an accuracy of the order of 500 m. The method improved the orientation to sub-pixel accuracy, similar to the results achieved by a commercial software package that implements a rigorous sensor model.