The development of the journal Spatial Statistics:The first 10 years

This paper presents an overview of the journal Spatial Statistics. It describes how it was initiated, how it developed and it highlights key moments from its young history. Starting in 2012, the journal has progressed in conjunction with the series of conferences in different countries over five continents. An important moment occurred when the journal received an impact factor in 2014. After a decline in the IF during the first years, the paper shows how the journal has now developed towards a more mature status, and is becoming a major scientific journal with a clearly defined niche. The paper describes in the end possible ways to move forward.</p

3D fully convolutional neural networks with intersection over union loss for crop mapping from multi-temporal satellite images

Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generate this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not take account of the spatial-temporal relationships inherent in remote sensing images. In our paper, we explore the capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types from multi-temporal images. In addition, we propose the Intersection Over Union (IOU) loss function for increasing the overlap between the predicted classes and ground reference data. The proposed method was applied to identify soybean and corn from a study area situated in the US corn belt using multi-temporal Landsat images. The study shows that our method outperforms related methods, obtaining a Kappa coefficient of 91.8%. We conclude that using the IOU loss function provides a superior choice to learn individual crop types.</p

Feature enhancement network for cloud removal in optical images by fusing with SAR images

Presence of cloud-covered pixels is inevitable in optical remote-sensing images. Therefore, the reconstruction of the cloud-covered details is important to improve the usage of these images for subsequent image analysis tasks. Aiming to tackle the issue of high computational resource requirements that hinder the application at scale, this paper proposes a Feature Enhancement Network(FENet) for removing clouds in satellite images by fusing Synthetic Aperture Radar (SAR) and optical images. The proposed network consists of designed Feature Aggregation Residual Block (FAResblock) and Feature Enhancement Block (FEBlock). FENet is evaluated on the publicly available SEN12MS-CR dataset and it achieves promising results compared to the benchmark and the state-of-the-art methods in terms of both visual quality and quantitative evaluation metrics. It proved that the proposed feature enhancement network is an effective solution for satellite image cloud removal using less computational and time consumption. The proposed network has the potential for practical applications in the field of remote sensing due to its effectiveness and efficiency. The developed code and trained model will be available at https://github.com/chenxiduan/FENet.</p

Sequence Information Encoded in DNA that May Influence Long-Range Chromatin Structure Correlates with Human Chromosome Functions.

Little is known about the possible function of the bulk of the human genome. We have recently shown that long-range regular oscillation in the motif non-T, A/T, G (VWG) existing at ten-nucleotide multiples influences large-scale nucleosome array formation. In this work, we have determined the locations of all 100 kb regions that are predicted to form distinctive chromatin structures throughout each human chromosome (except Y). Using these data, we found that a significantly greater fraction of 300 kb sequences lacked annotated transcripts in genomic DNA regions ≥300 kb that contained nearly continuous chromatin organizing signals than in control regions. We also found a relationship between the meiotic recombination frequency and the presence of strong VWG chromatin organizing signals. Large (≥300 kb) genomic DNA regions having low average recombination frequency are enriched in chromatin organizing signals. As additional controls, we show using chromosome 1 that the VWG motif signals are not enriched in randomly selected DNA regions having the mean size of the recombination coldspots, and that non-VWG motif sets do not generate signals that are enriched in recombination coldspots. We also show that tandemly repeated alpha satellite DNA contains strong VWG signals for the formation of distinctive nucleosome arrays, consistent with the low recombination activity of centromeres. Our correlations cannot be explained simply by variations in the GC content. Our findings suggest that a specific set of periodic DNA motifs encoded in genomic DNA, which provide signals for chromatin organization, influence human chromosome function

Region- and pixel-based image fusion for disaggregation of actual evapotranspiration

This paper compares a region-based and a pixel-based disaggregation method used to improve obtaining actual evapotranspiration (aET) data from MODIS images. Using these methods and the relationship between different vegetation indices form Landsat-5 and aET from MODIS, a 1 km resolution aET image was disaggregated to 250 and 30 m resolutions in two steps. Disaggregated aET images were compared with aET data obtained from a Landsat-5 TM image. A sensitivity analysis using synthetic data showed the impacts of land-cover homogeneity and registration error of the input images at the three scale levels. Accuracy assessment illustrated that the region-based disaggregation method using the Normalized Difference Vegetation Index (NDVI) has a good agreement with the Landsat-5 aET, having a mean absolute error equal to 0.93 mm. This method can be powerful for improving irrigation management, as it allows to increase the spatial resolution of aET derived from remote sensing images. The study concluded that a region-based method with NDVI data performs best to disaggregate MODIS aET data

Recurrent Multiresolution Convolutional Networks for VHR Image Classification

Classification of very high resolution (VHR) satellite images has three major challenges: 1) inherent low intra-class and high inter-class spectral similarities, 2) mismatching resolution of available bands, and 3) the need to regularize noisy classification maps. Conventional methods have addressed these challenges by adopting separate stages of image fusion, feature extraction, and post-classification map regularization. These processing stages, however, are not jointly optimizing the classification task at hand. In this study, we propose a single-stage framework embedding the processing stages in a recurrent multiresolution convolutional network trained in an end-to-end manner. The feedforward version of the network, called FuseNet, aims to match the resolution of the panchromatic and multispectral bands in a VHR image using convolutional layers with corresponding downsampling and upsampling operations. Contextual label information is incorporated into FuseNet by means of a recurrent version called ReuseNet. We compared FuseNet and ReuseNet against the use of separate processing steps for both image fusion, e.g. pansharpening and resampling through interpolation, and map regularization such as conditional random fields. We carried out our experiments on a land cover classification task using a Worldview-03 image of Quezon City, Philippines and the ISPRS 2D semantic labeling benchmark dataset of Vaihingen, Germany. FuseNet and ReuseNet surpass the baseline approaches in both quantitative and qualitative results