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The goal of this paper is to propose a method to achieve a higher classification rate in Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In our work, PolSAR features are extracted from Convolutional Neural Networks (CNNs) and also Graph Convolutional Networks (GCNs). Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a substantial computational cost, particularly in large‐scale remote sensing (RS) problems. To this end, first of all, we propose a new mini‐batch GCN (called Mini‐GCN), which allows to train large‐scale GCNs in a mini‐batch fashion. More significantly, our designed mini‐GCN is capable of inferring out‐of‐sample data without re‐training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of PolSAR features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since mini‐GCNs can perform batch‐wise network training (enabling the combination of CNNs and GCNs), we can use the feature fusion strategy. Therefore, in this paper, a local graph‐based fusion method is proposed to couple dimension reduction and feature fusion of the information extracted from the designed CNN and Mini‐GCN. Experimental results on real PolSAR data are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed feature fusion method improves the overall classification accuracy on the real PolSAR data sets for more than 5%. Moreover, the experiments conducted on PolSAR datasets, demonstrate the advantages of the used mini‐GCNs over the traditional GCNs and the superiority of the proposed feature fusion method with regards to the single CNN or GCN models. [ABSTRACT FROM AUTHOR]

Criteria for the localization of gold deposits within the Ayan–Yuryakh anticlinorium in Magadan oblast have been developed using processed satellite imagery data and the analysis of geological and geophysical materials. The structural elements are linear, arc, ring, and concentric lineaments of the first (11–35 km), second (5–10 km), and third (less than 5 km) rank. The ring structures of the second and third rank form linear chains confined to the hidden linear structures of the northwest and north-northeast strike. Gold deposits occur in the central or peripheral parts of the ring structures of the second rank at the intersections of the latter with regional sublatitudinal faults. The area of ​​the Pavlik deposit has a block structure, due to the dominant faults of the northwestern strike, complicated by faults of the northeastern and meridional strike, as well as arc structural elements. When predicting prospective ore areas, the localization patterns of gold ore, determined by the results of gravimetric and gamma surveys, should be taken into account. [ABSTRACT FROM AUTHOR]

PyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages. [ABSTRACT FROM AUTHOR]

This contribution proposes an evaluation of lidar and radar data processing and its potential in revealing archaeological features within a level plain environment, the southern lowland of Verona (Italy), focusing on evidences dating back to the Bronze Age. Many archaeological sites in the research area, including some of the most outstanding settlements of Terramare Culture, were identified or at least examined through aerial photo observation. Even if in several occasions modern agricultural activities contributed to the discoveries, bringing to the surface artifacts and scrapes of buried layers, this kind of impact has also been progressively deteriorating the archaeological record, hence the proto-historic landscape is now discernible through evanescent marks which cannot be always detected using customary optical sensors. Lidar and radar data analysis has then been considered as an alternative, non-invasive method of investigation on such a vast area. [ABSTRACT FROM AUTHOR]

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory. [ABSTRACT FROM PUBLISHER]

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF. [ABSTRACT FROM AUTHOR]

The coastal zone offers among the world's most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications. [ABSTRACT FROM AUTHOR]

Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/ development of new data analysis tools. In this editorial paper we provide the setting of the special issue "Computational Intelligence in Remote Sensing" and an overview of the published papers. The 11 accepted and published papers cover a wide spectrum of applications and computational tools that we try to summarize and put in perspective in this editorial paper. [ABSTRACT FROM AUTHOR]