A case study for a multitemporal segmentation approach in optical remote sensing images.

Continuous observations from remote sensors provide high temporal and spatial resolution imagery, and better remote sensing image segmentation techniques are mandatory for efficient analysis. Among them, one of the most applied segmentation techniques is the region growing algorithm. Within this context, this paper describes a study case for a multitemporal segmentation that adapts the traditional region growing technique. Our method aims to detect homogeneous regions in space and time observing a sequence of optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping distance as the homogeneity criterion to grow regions. A case study on high temporal resolution for sequences of Landsat-8 vegetation indices products provided satisfactory outputs.GEOProcessing 2018

Segmentation of optical remote sensing images for detecting homogeneous regions in space and time.

With the amount of multitemporal and multiresolution images growing exponentially, the number of image segmentation applications is recently increasing and, simultaneously, new challenges arise. Hence, there is a need to explore new segmentation concepts and techniques that make use of the temporal dimension. This paper describes a spatio-temporal segmentation that adapts the traditional region growing technique to detect homogeneous regions in space and time in optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping measure as the homogeneity criterion. Study cases on high temporal resolution for sequences of MODIS and Landsat-8 OLI vegetation indices products provided satisfactory outputs and demonstrated the potential of the spatio-temporal segmentation method.Também publicado na Revista Brasileira de Cartografia, v. 70, n. 5, p. 1779-1801, 2018. Special Issue XIX Brazilian Syposium on GeoInformatics, 2018. DOI: 10.14393/rbcv70n5-45227

Oncostatin-M inhibits luteinizing hormone stimulated Leydig cell progenitor formation in vitro

Background: The initial steps of stem Leydig cell differentiation into steroid producing progenitor cells are thought to take place independent of luteinizing hormone (LH), under the influence of locally produced factors such as leukaemia inhibitory factor (LIF), platelet derived growth factor A and stem cell factor. For the formation of a normal sized Leydig cell population in the adult testis, the presence of LH appears to be essential. Oncostatin M (OSM) is a multifunctional cytokine and member of the interleukin (IL)-6 family that also includes other cytokines such as LIF. In the rat OSM is highly expressed in the late fetal and neonatal testis, and may thus be a candidate factor involved in Leydig cell progenitor formation. Methods: Interstitial cells were isolated from 13-day-old rat testes and cultured for 1, 3 or 8 days in the presence of different doses of OSM ( range: 0.01 to 10 ng/ml) alone or in combination with LH ( 1 ng/ml). The effects of OSM and LH on cell proliferation were determined by incubating the cultures with [3H] thymidine or bromodeoxyuridine ( BrdU). Developing progenitor cells were identified histochemically by the presence of the marker enzyme 3beta-hydroxysteroid dehydrogenase (3beta-HSD). Results: OSM, when added at a dose of 10 ng/ml, caused a nearly 2-fold increase in the percentage of Leydig cell progenitors after 8 days of culture. Immunohistochemical double labelling experiments with 3beta-HSD and BrdU antibodies showed that this increase was the result of differentiation of stem Leydig cells/precursor cells and not caused by proliferation of progenitor cells themselves. The addition of LH to the cultures consistently resulted in an increase in progenitor formation throughout the culture period. Surprisingly, when OSM and LH were added together, the LH induced rise in progenitor cells was significantly inhibited after 3 and 8 days of culture. Conclusion: Taken together, the results of the present study suggest that locally produced OSM may not only play a role in the regulation of Sertoli cell proliferation and the initiation of spermatogenesis but may also play a role in the regulation of Leydig cell progenitor formation by keeping the augmenting effects of LH on this process in abeyance

Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies based on deep learning

The Brazilian Savanna, also known as Cerrado, is considered a global hotspot for biodiversity conservation. The detailed mapping of vegetation types, called physiognomies, is still a challenge due to their high spectral similarity and spatial variability. There are three major ecosystem groups (forest, savanna, and grassland), which can be hierarchically subdivided into 25 detailed physiognomies, according to a well-known classification system. We used an adapted U-net architecture to process a WorldView-2 image with 2-m spatial resolution to hierarchically classify the physiognomies of a Cerrado protected area based on deep learning techniques. Several spectral channels were tested as input datasets to classify the three major ecosystem groups (first level of classification). The dataset composed of RGB bands plus 2-band enhanced vegetation index (EVI2) achieved the best performance and was used to perform the hierarchical classification. In the first level of classification, the overall accuracy was 92.8%. On the other hand, for the savanna and grassland detailed physiognomies (second level of classification), 86.1% and 85.0% were reached, respectively. As the first work that intended to classify Cerrado physiognomies in this level of detail using deep learning, our accuracy rates outperformed others that applied traditional machine learning algorithms for this task

Using Landsat 8 image time series for crop mapping in a region of Cerrado, Brazil.

Abstract: The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification

Identification of gaps in sugarcane plantations using UAV images.

The objective of this study is to present a methodology for the detection and quantification of gaps formed during planting or growing of sugarcane crops. The use of UAV images for precision agriculture is relevant because it brings new possibilities for improving crop's productivity by feeding the producer with highly accurate data about the crop status

Using mixed objects in the training of object-based image classifications

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition and thus violate the commonly made assumption of object purity that is implicit in a conventional object-based image analysis. Mixed objects can be a problem throughout a classification analysis, but are particularly challenging in the training stage as they can result in degraded training statistics and act to reduce mapping accuracy. In this paper the potential of using mixed objects in training object-based image classifications is evaluated. Remotely sensed data were submitted to a series of segmentation analyses from which a range of under- to over-segmented outputs were intentionally produced. Training objects were then selected from the segmentation outputs, resulting in training data sets that varied in terms of size (i.e. number of objects) and proportion of mixed objects. These training data sets were then used with an artificial neural network and a generalized linear model, which can accommodate objects of mixed composition, to produce a series of land cover maps. The use of training statistics estimated based on both pure and mixed objects often increased classification accuracy by around 25% when compared with accuracies obtained from the use of only pure objects in training. So rather than the mixed objects being a problem, they can be an asset in classification and facilitate land cover mapping from remote sensing. It is, therefore, desirable to recognize the nature of the objects and possibly accommodate mixed objects directly in training. The results obtained here may also have implications for the common practice of seeking an optimal segmentation output, and also act to challenge the widespread view that object-based classification is superior to pixel-based classification

Ergebnisse dislozierter proximaler Humerusfrakturen nach Versorgung mit einem inversen Prothesensystem

Fragestellung: Zur Versorgung proximaler Humerusfrakturen stehen verschiedene Therapieoptionen zur Verfügung. Anhand dieser Studie sollen die Ergebnisse nach operativer Versorgung mittels inversem Prothesensystem ermittelt werden. Methodik: In der Studie wurden 34 Patienten mit 35 Prothesen (m=3, w=31, Alter= 77,5 J.) nach sechs und nach mindestens 12 Monaten klinisch (Constant-Score und ASES-Score) und radiologisch (Tubercula, Lockerungszeichen, Skapula-Notching) nachuntersucht. Ergebnisse: Der Constant Score lag nach 31,5 Monaten bei 62,2 Punkten. Es zeigte sich eine signifikante Verbesserung des relativen CS (6 Mon. 75,9%, mind. 24 Mon. 94,1%). Der ASES lag bei 75 Punkten. Der mittlere Schmerzscore betrug 0,7/10 (VAS). Die Gesamtkomplikationsrate betrug 20%. Schlussfolgerung: Die inverse Schulterprothese zeigt zufriedenstellende Ergebnisse vor allem in Schmerzreduktion und Motilität nach der operativen Versorgung dislozierter proximaler Humerusfrakturen älterer Patienten