Time-Space Tradeoff in Deep Learning Models for Crop Classification on Satellite Multi-Spectral Image Time Series

International audienceIn this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification

The chameleon groups of Richard J. Thompson: automorphisms and dynamics

The automorphism groups of several of Thompson's countable groups of piecewise linear homeomorphisms of the line and circle are computed and it is shown that the outer automorphism groups of these groups are relatively small. These results can be interpreted as stability results for certain structures of PL functions on the circle. Machinery is developed to relate the structures on the circle to corresponding structures on the line

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

Modulation of IL-17 and Foxp3 Expression in the Prevention of Autoimmune Arthritis in Mice

©2010 Duarte et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Rheumatoid Arthritis (RA) is a chronic immune mediated disease associated with deregulation of many cell types. It has been reported that different T cell subsets have opposite effects in disease pathogenesis, in particular Th17 and Treg cells. Methodology and Findings: We investigated whether non-depleting anti-CD4 monoclonal antibodies, which have been reported as pro-tolerogenic, can lead to protection from chronic autoimmune arthritis in SKG mice – a recently described animal model of RA – by influencing the Th17/Treg balance. We found that non-depleting anti-CD4 prevented the onset of chronic autoimmune arthritis in SKG mice. Moreover, treated mice were protected from the induction of arthritis up to 60 days following anti-CD4 treatment, while remaining able to mount CD4-dependent immune responses to unrelated antigens. The antibody treatment also prevented disease progression in arthritic mice, although without leading to remission. Protection from arthritis was associated with an increased ratio of Foxp3, and decreased IL-17 producing T cells in the synovia. In vitro assays under Th17-polarizing conditions showed CD4-blockade prevents Th17 polarization, while favoring Foxp3 induction. Conclusions: Non-depleting anti-CD4 can therefore induce long-term protection from chronic autoimmune arthritis in SKG mice through reciprocal changes in the frequency of Treg and Th17 cells in peripheral tissues, thus shifting the balance towards immune tolerance.This work was funded by SUDOE, grant number IMMUNONET-SOE1/1P1/E014, and supported by a research grant from Fundação para a Ciência e Tecnologia (FCT), Portugal (FCT/POCI/SAU-MMO/55974/2004). JD, AA-D, and VGO are funded with scholarships from FCT (SFRH/BD/23631/2005, SFRH/BD/49093/2008, and SFRH/BPD/22575/2005)

CHANGE DETECTION IN A TOPOGRAPHIC BUILDING DATABASE USING SUBMETRIC SATELLITE IMAGES

Submetric satellite imagery (Pleiades, GeoEye) offers advantages for map update purposes, e.g. an interesting ground resolution, a good reactivity and the ability to capture wide areas. Experiments on the use of such stereoscopic images for 2D change detection among building objects of GIS topographic database are presented in this paper. Two approaches have been tested. The first one extracts land cover from satellite ortho-images and additional information (correlation DSM-DTM, database) and compares building objects of this classification to those of the database. The second one creates a pseudo-DSM from height information of database building objects combined with a DTM and compares it to a correlation DSM computed from satellite images. Obtained results are quite encouraging even if the correctness rate remains too low for an operational use

SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) to material classification was also shown

OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES

An object-based approach for forest disaster change detection using High Resolution (HR) satellite images is proposed. An automatic feature selection process is used to optimize image segmentation via an original calibration-like procedure. A multitemporal classification then enables the separation of wind-fall from intact areas based on a new descriptor that depends on the level of fragmentation of the detected regions. The mean shift algorithm was used in both the segmentation and the classification processes. The method was tested on a high resolution Formosat-2 multispectral satellite image pair acquired before and after the Klaus storm. The obtained results are encouraging and the contribution of high resolution images for forest disaster mapping is discussed

Extraction of optimal spectral bands using hierarchical band merging out of hyperspectral data

International audienceSpectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspec-tral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands

Crop-Rotation Structured Classification using Multi-Source Sentinel Images and LPIS for Crop Type Mapping

International audienceAutomatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel Identification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km 2 study area located in France and with a 25 categories national nomenclature. The classification results are globally improved