Lourenço P. C. Bandeira

Impact Crater Recognition on Mars Based on a Probability Volume Created by Template Matching

This paper presents a methodology that brings together a number of techniques in the fields of image processing and pattern recognition with the purpose of achieving the automated detection of impact craters on images of planetary surfaces. The modular approach adopted for its development includes a phase of candidate selection, followed by template matching, in which the probability associated to each detection is established, and finally, by the analysis of the probability volume, in which the identification of craters on the image is achieved. It is tested on a set of images from four different regions of the surface of the planet Mars, all obtained by the same sensor in the last decade. The recognition rates for craters with radii that are larger than five pixels are very good, both globally and for each of the individual areas. The performance of the algorithm in the face of the variation of some of its parameters is analyzed and discussed in detail. We believe that this is a tool that is suitable for a general application in any area of a planet or satellite captured in an image, whatever the geomorphological setting, the optical sensor, and the conditions of illumination are.

Subkilometer crater discovery with boosting and transfer learning

Counting craters in remotely sensed images is the only tool that provides relative dating of remote planetary surfaces. Surveying craters requires counting a large amount of small subkilometer craters, which calls for highly efficient automatic crater detection. In this article, we present an integrated framework on autodetection of subkilometer craters with boosting and transfer learning. The framework contains three key components. First, we utilize mathematical morphology to efficiently identify crater candidates, the regions of an image that can potentially contain craters. Only those regions occupying relatively small portions of the original image are the subjects of further processing. Second, we extract and select image texture features, in combination with supervised boosting ensemble learning algorithms, to accurately classify crater candidates into craters and noncraters. Third, we integrate transfer learning into boosting, to enhance detection performance in the regions where surface morphology differs from what is characterized by the training set. Our framework is evaluated on a large test image of 37,500 × 56,250 m2 on Mars, which exhibits a heavily cratered Martian terrain characterized by nonuniform surface morphology. Empirical studies demonstrate that the proposed crater detection framework can achieve an F1 score above 0.85, a significant improvement over the other crater detection algorithms.

Development of a Methodology for Automated Crater Detection on Planetary Images

This paper presents a methodology for the automated detection of impact craters on images of planetary surfaces. This modular approach includes a phase of candidate selection, followed by template matching and finally the analysis of a probability volume that allows for the identification of craters on the image. It is applied to a set of images of the surface of the planet Mars, with results that are very promising, in face of future improvements in the methodology.

Automated Detection of Martian Dune Fields

An approach for the automated detection of dune fields on remotely sensed images of the surface of Mars is presented in this letter. It is based on the extraction of local information from images (i.e., gradient features), which, in turn, is tested with boosting and support vector machine classifiers. A detection rate of about 95% is obtained for fivefold cross validation on a set of 78 panchromatic images captured by the Mars Orbiter Camera of the Mars Global Surveyor probe on different locations of the planet.

Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting

Identifying impact craters on planetary surfaces is one fundamental task in planetary science. In this paper, we present an embedded framework on auto-detection of craters, using feature selection and boosting strategies. The paradigm aims at building a universal and practical crater detector. This methodology addresses three issues that such a tool must possess: (i) it utilizes mathematical morphology to efficiently identify the regions of an image that can potentially contain craters; only those regions, defined as crater candidates, are the subjects of further processing; (ii) it selects Haar-like image texture features in combination with boosting ensemble supervised learning algorithms to accurately classify candidates into craters and non-craters; (iii) it uses transfer learning, at a minimum additional cost, to enable maintaining an accurate auto-detection of craters on new images, having morphology different from what has been captured by the original training set. All three aforementioned components of the detection methodology are discussed, and the entire framework is evaluated on a large test image of 37,500 x 56,250

Identification of martian polygonal patterns using the dynamics of watershed contours

m2 on Mars, showing heavily cratered Martian terrain characterized by nonuniform surface morphology. Our study demonstrates that this methodology provides a robust and practical tool for planetary science, in terms of both detection accuracy and efficiency.

A multi-layer approach for the analysis of neighbourhood relations of polygons in remotely acquired images

This paper presents a methodology to automatically identify polygonal patterns on the surface of Mars. These structures, which are typical of periglacial regions, result from climate oscillations and present a wide variation in size, shape and topology and occur in different types of terrains with rather different constituents and spectral reflectances. The proposed approach is mainly based on the analysis of the dynamics of watershed contours and is successfully applied to a set of different types of patterned terrains of Mars shown by MGS/MOC images.

Polygonal networks on the surface of Mars; applicability of Lewis, Desch and Aboav–Weaire laws

In this letter we present a novel approach to extract topological features of polygonal networks, based on a multi-layer strategy; the motivation for this new development was the need to analyse the small-scale polygonal patterns observed on remotely sensed, high spatial resolution images of the surface of Mars. The major improvement of the algorithm consists of the distribution of polygons by layers in such a way that adjacent polygons cannot coexist on any given layer; this is followed by a global analysis of each layer to extract topological features. This novel approach can be indistinctively applied to any kind of tri and tetravalent network (presenting respectively three and four polygons at each vertex); its computational performance is extremely favourable when compared with previous approaches to this problem. The experimental dataset used to evaluate the algorithm consisted of 47 segmented polygonal networks seen on the surface of Mars and presenting very distinct visual appearances.

Combining fuzzy clustering and morphological methods for old documents recovery

Verification of the applicability of Lewis, Desch and Aboav–Weaire laws to polygonal networks in terrains on the surface of Mars is reported. The networks analysed cover a great variety of small-scale polygonal terrains formed in periglacial regions of the planet. It is shown that these Martian terrains share some of the same geometrical and topological properties of other random networks built by materials and processes of diversified nature and origin.

Evaluation of the use of very high resolution aerial imagery for accurate ice-wedge polygon mapping (Adventdalen, Svalbard)

In this paper we tackle the specific problem of old documents recovery. Spots, print through, underlines and others ageing features are undesirable not only because they harm the visual appearance of the document, but also because they affect future Optical Character Recognition (OCR). This paper proposes a new method integrating fuzzy clustering of color properties of original images and mathematical morphology. We will show that this technique leads to higher quality of the recovered images and, at the same time, it delivers cleaned binary text for OCR applications. The proposed method was applied to books of XIX Century, which were cleaned in a very effective way.