M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion

Modern Earth Observation systems provide sensing data at different temporal and spatial resolutions. Among optical sensors, today the Sentinel-2 program supplies high-resolution temporal (every 5 days) and high spatial resolution (10m) images that can be useful to monitor land cover dynamics. On the other hand, Very High Spatial Resolution images (VHSR) are still an essential tool to figure out land cover mapping characterized by fine spatial patterns. Understand how to efficiently leverage these complementary sources of information together to deal with land cover mapping is still challenging. With the aim to tackle land cover mapping through the fusion of multi-temporal High Spatial Resolution and Very High Spatial Resolution satellite images, we propose an End-to-End Deep Learning framework, named M3Fusion, able to leverage simultaneously the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR information. Experiments carried out on the Reunion Island study area asses the quality of our proposal considering both quantitative and qualitative aspects

Avaliação do impacto das amostras de treinamento na acurácia da classificação random forest dos sistemas integrados de produção agropecuária.

Ao conduzir uma classificação supervisionada com algoritmos de aprendizado de máquina, como o Random Forest, a estratégia de balanceamento das amostras é fundamental, pois impacta diretamente nos resultados. Estes classificadores são sensíveis às proporções das amostras de treinamento das diferentes classes. Compreender como estes fatores influenciam na classificação de áreas de produção agropecuária, sobretudo de sistemas minoritários e complexos como o iLP (Integração Lavoura-Pecuária) são de extrema importância para contribuir com metodologias de monitoramento. Para avaliar o impacto do balanceamento, foram testados três grupos de dados de aprendizagem do Random Forest: (i) Bset01: dados balanceados entre três classes prioritárias no estado do Mato Grosso; (ii) Bset02: dados desbalanceados com as proporções refletindo a realidade de campo e (iii) Bset03: superestimando a classe rara iLP. Os melhores valores de fscore da classe iLP foram para Bset01 (0,81) e Bset02 (0,83), com um erro de comissão mais alto para Bset01, sugerindo uma melhor performance do Bset02

High-Dimensional Satellite Image Compositing and Statisticsfor Enhanced Irrigated Crop Mapping

Accurate irrigated area maps remain difficult to generate, as smallholder irrigation schemes often escape detection. Efforts to map smallholder irrigation have often relied on complex classification models fitted to temporal image stacks. The use of high-dimensional geometric median composites (geomedians) and high-dimensional statistics of time-series may simplify classification models and enhance accuracy. High-dimensional statistics for temporal variation, such as the spectral median absolute deviation, indicate spectral variability within a period contributing to a geomedian. The Ord River Irrigation Area was used to validate Digital Earth Australia’s annual geomedian and temporal variation products. Geomedian composites and the spectral median absolute deviation were then calculated on Sentinel-2 images for three smallholder irrigation schemes in Matabeleland, Zimbabwe, none of which were classified as areas equipped for irrigation in AQUASTAT’s Global Map of Irrigated Areas. Supervised random forest classification was applied to all sites. For the three Matabeleland sites, the average Kappa coefficient was 0.87 and overall accuracy was 95.9% on validation data. This compared with 0.12 and 77.2%, respectively, for the Food and Agriculture Organisation’s Water Productivity through Open access of Remotely sensed derived data (WaPOR) land use classification map. The spectral median absolute deviation was ranked among the most important variables across all models based on mean decrease in accuracy. Change detection capacity also means the spectral median absolute deviation has some advantages for cropland mapping over indices such as the Normalized Difference Vegetation Index. The method demonstrated shows potential to be deployed across countries and regions where smallholder irrigation schemes account for large proportions of irrigated area.This research was undertaken while supported by the Australian National University (ANU) University Research Scholarship and a Commonwealth Scientific and Industrial Research Organisation (CSIRO) and ANU Digital Agriculture Supplementary Scholarship through the Centre for Entrepreneurial AgriTechnology

Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images

This paper presents a semantic edge-aware multi-task neural network (SEANet) to obtain closed boundaries when delineating agricultural parcels from remote sensing images. It derives closed boundaries from remote sensing images and improves conventional semantic segmentation methods for the extraction of small and irregular agricultural parcels. SEANet integrates three correlated tasks: mask prediction, edge prediction, and distance map estimation. Related features learned from these tasks improve the generalizability of the network. We regard boundary extraction as an edge detection task and extract rich semantic edge features at multiple levels to improve the geometric accuracy of parcel delineation. Moreover, we develop a new multi-task loss that considers the uncertainty of different tasks. We conducted experiments on three high-resolution Gaofen-2 images in Shandong, Xinjiang, and Sichuan provinces, China, and on two medium-resolution Sentinel-2 images from Denmark and the Netherlands. Results showed that our method produced a better layout of agricultural parcels, with higher attribute and geometric accuracy than the existing ResUNet, ResUNet-a, R2UNet, and BsiNet methods on the Shandong and Denmark datasets. The total extraction errors of the parcels produced by our method were 0.214, 0.127, 0.176, 0.211, and 0.184 for the five datasets, respectively. Our method also obtains closed boundaries by one single segmentation, leading to superiority as compared with existing multi-task networks. We showed that it could be applied to images with different spatial resolutions for parcel delineation. Finally, our method trained on the Xinjiang dataset could be successfully transferred to the Shandong dataset with different dates and landscapes. Similarly, we obtained satisfactory results when transferring from the Denmark dataset to the Netherlands dataset. We conclude that SEANet is an accurate, robust, and transferable method for various areas and different remote sensing images. The codes of our model are available at https://github.com/long123524/SEANet_torch.</p

Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs

Very shallow coral reefs (<5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products into ecologically relevant habitat maps are still relatively underdeveloped. Here, we demonstrate that incorporating geomorphometric variables (derived from the DEM) in addition to spectral information (derived from the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM and classified through object-based image analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV’s DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset

EUNIS habitat identification and classification by multispectral imagery in ría de Ortigueira (Galicia)

[ES] Contar con una cartografía de hábitats actualizada y precisa es un aspecto clave a la hora de analizar y gestionar espacios naturales, por ello, la teledetección tiene un papel fundamental. En este caso el análisis se ha centrado en una parte de la Ría de Ortigueira y Ladrido (A Coruña), incluida en diversos instrumentos de protección debido a su relevancia ecológica y a la fragilidad de este tipo de ecosistemas. En este trabajo se plantea como objetivo principal realizar clasificaciones supervisadas empleando análisis orientado a objetos a partir de imágenes multiespectrales de dos sensores con diferentes características e información complementaria, en base a las categorías de hábitats establecidas en el sistema EUNIS (European Nature Information System). Posteriormente se analizarán los resultados obtenidos y las variables más representativas en cada caso. Los pasos seguidos han sido los siguientes: descarga y procesado de las imágenes e información complementaria, segmentación de la ortofotografía del PNOA (Plan Nacional de Ortofotografía Aérea) para utilizarla como punto de partida en ambos casos, preparación de la base de datos formada por las diferentes variables y sus métricas, cálculo de estadísticas zonales para cada objeto, realización de clasificaciones supervisadas mediante algoritmos de aprendizaje automático y validación de los resultados empleando diferentes estadísticos. Estos procesos se han realizado empleando una combinación de diferentes softwares, tanto comerciales como libres. Los resultados muestran mayor precisión global en la clasificación generada en base a las imágenes de WorldView 2 en comparación con la realizada con Sentinel 2. Además, en ambos casos se observa la importancia que presentan los índices de vegetación a la hora de caracterizar los diferentes hábitat