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

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

PRISMA and Sentinel-2 spectral response to the nutrient composition of grains

Micronutrient malnutrition is a global challenge affecting &gt;2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest. We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.</p

Recommender-based enhancement of discovery in Geoportals

Abstract In many cases web search engines like Google are still used for discovery of geographic base information. This can be explained by the fact that existing approaches for Geo-information retrieval still face significant challenges. Discovery in currently available Geoportals is usually restricted to text-based search based on keywords, title and abstract as well as applying spatial and temporal filters. Furthermore, user context as well as search results of other users are not incorporated. In order to improve the quality of search results we propose to extend the suitable searching matches in Geoportals with user behaviour and to present them as non-directly linked recommendations like in e.g. Amazon&apos;s &quot;Customers Who Bought This Item Also Bought&quot; approach. As shown in the proof-of-concept EU FP7 EnerGEO Geoportal, it guarantees results that are not in the data itself but rather derived from the context of other users&apos; searches and views

Recommender-based enhancement of discovery in Geoportals

In many cases web search engines like Google are still used for discovery of geographic base information. This can be explained by the fact that existing approaches for Geo-information retrieval still face significant challenges. Discovery in currently available Geoportals is usually restricted to text-based search based on keywords, title and abstract as well as applying spatial and temporal filters. Furthermore, user context as well as search results of other users are not incorporated. In order to improve the quality of search results we propose to extend the suitable searching matches in Geoportals with user behaviour and to present them as non-directly linked recommendations like in e.g. Amazon's “Customers Who Bought This Item Also Bought” approach. As shown in the proof-of-concept EU FP7 EnerGEO Geoportal, it guarantees results that are not in the data itself but rather derived from the context of other users’ searches and views