Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks

Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural Networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper we present a CNN-based system relying on an downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including i) state-of-the-art numerical accuracy, ii) improved geometric accuracy of predictions and iii) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam sub-decimeter resolution datasets, involving semantic labeling of aerial images of 9cm and 5cm resolution, respectively. These datasets are composed by many large and fully annotated tiles allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures to the proposed one: standard patch classification, prediction of local label patches by employing only convolutions and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.Comment: Accepted in IEEE Transactions on Geoscience and Remote Sensing, 201

Detecting animals in African Savanna with UAVs and the crowds

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs

Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models

In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object's orientation and on a sensor's flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way

Interactions between Natural Health Products and Oral Anticoagulants: Spontaneous Reports in the Italian Surveillance System of Natural Health Products

Introduction. The safety of vitamin K antagonists (VKA) use can be compromised by many popular herbal supplements taken by individuals. The literature reports that 30% of warfarin-treated patients self-medicates with herbs. Possible interactions represent an health risk. We aimed to identify all herbs-oral anticoagulants interactions collected in the Italian database of suspected adverse reactions to “natural health” products. Methods. The Italian database of spontaneous reports of suspected adverse reactions to natural products was analyzed to address herb-VKAs interactions. Results. From 2002 to 2009, we identified 12 reports with 7 cases of INR reduction in patients treated with warfarin (n = 3) and acenocoumarol (n = 4), and 5 cases of INR increase (all warfarin associated). It was reported 8 different herbal products as possibly interacting. Discussion. Our study confirms the risk of interactions, highlighting the difficulty to characterize them and their mechanisms and, finally, prevent their onset. The reported data underline the urgent need of healthcare providers being aware of the possible interaction between natural products and VKA, also because of the critical clinical conditions affecting patients. This is the first step to have the best approach to understand possible INR alterations linked to herb-VKA interaction and to rightly educate patients in treatment with VKA

Serum gamma-glutamyltransferase fractions in Myotonic Dystrophy type I: Differences with healthy subjects and patients with liver disease.

Objectives: Elevation of serum gamma-glutamyltransferase (GGT), in absence of a clinically significant liver damage, is often found in Myotonic Dystrophy type-1 (DM1). In this study we investigated if a specific GGT fraction pattern is present in DM1. Designs and methods: We compared total and fractional GGT values (b-, m-, s-, f-GGT) among patients with DM1 or liver disease (LD) and healthy subjects (HS). Results: The increase of GGT in DM1 and LD, vs HS, was mainly due to s-GGT (median: 32.7; 66.7; and 7.9 U/L, respectively), and b-GGT (8.5; 18.9; and 2.1 U/L). The subset of DM1 patients matched with HS with corresponding serum GGT showed higher b-GGT (6.0 vs 4.2 U/L). Conclusions: DM1 patients with normal total GGT values showed an alteration of the production and release in the blood of GGT fractions. Since increased s-GGT is also found in LD, a sub-clinical liver damage likely occurs in DM1 subjects apparently free of liver disease

Rheumatic heart disease with triple valve involvement

Acute rheumatic fever (ARF) is a postinfectious, nonsuppurative sequela of pharyngeal infection caused by Streptococcus pyogenes, or Group A β hemolytic Streptococcus (GABHS). Of the associated symptoms, only damage to the heart’s valvular tissue, or rheumatic heart disease (RHD), can become a chronic condition leading to congestive heart failure, stroke, endocarditis, and death. ARF is the most common cause of cardiac disease in children in developing countries. A joint meeting of the World Health Organization and the International Society estimated that 12 million people in developing countries were affected by acute rheumatic fever and rheumatic heart disease, with the majority of these being children. This level of morbidity is comparable to developed countries’ in the last century, before an increase in the standard of living and the introduction of penicillin. Significant trivalvular disease, involving the mitral, aortic and tricuspid valves, is uncommon. Although rare, trivalvular disease has been described in the literature. Clinical and hemodynamic manifestations depend on the severity of each lesion. We reported this case because of the rare presentation of an uncommon disorder and to highlight the fact that the presence of trivalvular disease can be difficult to diagnose, even for a trained physician