An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning

Precision viticulture benefits from the accurate detection of vineyard vegetation from remote sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient, and potentially automated, derivation of spatial measures such as length and area of crop, and hence required volumes of water, fertilizer, and other resources. Machine learning techniques have provided significant advancements in recent years in the areas of image segmentation, classification, and object detection, with neural networks shown to perform well in the detection of vineyards and other crops. However, what has not been extensively quantitatively examined is the extent to which the initial choice of input imagery impacts detection/segmentation accuracy. Here, we use a standard deep convolutional neural network (CNN) to detect and segment vineyards across Australia using DigitalGlobe Worldview-2 images at ∼50 cm (panchromatic) and ∼2 m (multispectral) spatial resolution. A quantitative assessment of the variation in model performance with input parameters during model training is presented from a remote sensing perspective, with combinations of panchromatic, multispectral, pan-sharpened multispectral, and the spectral Normalised Difference Vegetation Index (NDVI) considered. The impact of image acquisition parameters—namely, the off-nadir angle and solar elevation angle—on the quality of pan-sharpening is also assessed. The results are synthesised into a ‘recipe’ for optimising the accuracy of vineyard segmentation, which can provide a guide to others aiming to implement or improve automated crop detection and classification.

Computer-Aided Exploration of the Martian Geology

Motivated by growing amounts of data and enhanced resolution from orbiters and rovers, systems for computer-aided decision support are becoming invaluable in planetary exploration. This article illustrates the value of such systems for a case study on the exploration of the Martian geology, along with improvements in assessing the favorability for landing. Under the current technical status quo for landing and rover's mobility, results show that Eastern Margaritifer Terra and Meridiani Planum stand out due to their high density of scientific targets and flat surfaces. However, our approach allows us to scale the analysis using different scenarios for the entire planet, quantifying the substantial benefits should higher landing elevations and higher rover speeds be realized in the future. This analysis offers new insights into the interplay of technical and scientific constraints.

A record of igneous evolution in Elysium, a major martian volcanic province

A major knowledge gap exists on how eruptive compositions of a single martian volcanic province change over time. Here we seek to fill that gap by assessing the compositional evolution of Elysium, a major martian volcanic province. A unique geochemical signature overlaps with the southeastern flows of this volcano, which provides the context for this study of variability of martian magmatism. The southeastern lava fields of Elysium Planitia show distinct chemistry in the shallow subsurface (down to several decimeters) relative to the rest of the martian mid-to-low latitudes (average crust) and flows in northwest Elysium. By impact crater counting chronology we estimated the age of the southeastern province to be 0.85 ± 0.08 Ga younger than the northwestern fields. This study of the geochemical and temporal differences between the NW and SE Elysium lava fields is the first to demonstrate compositional variation within a single volcanic province on Mars. We interpret the geochemical and temporal differences between the SE and NW lava fields to be consistent with primary magmatic processes, such as mantle heterogeneity or change in depth of melt formation within the martian mantle due to crustal loading.