Remote sensing

Greenhouse detection and classification from satellite images

Plastic-covered agriculture is part of the transformation of the conventional farming into a more industrial and 'high-tech' agriculture. Unfortunately, plasticulture has significant anthropic impact: careful spatial development planning is therefore required to minimise the potential downsides of this type of cultivation. Satellite imagery represents a powerful means for local governments and environmental agencies to monitor the use of soil and greenhouse development. Our investigation involves the use of image stereo-pairs from GeoEye-1 and WorldView-2 for object-based detection and classification of plastic- or net- covered greenhouses.

In the picture: ground truth of land cover use in the municipality of Cuevas del Almanzora, Almería, Spain. From Aguilar, M.A. et al. Object-based greenhouse classification from GeoEye-1 and WorldView-2 stereo imagery (2014) Remote Sensing, 6 (5), pp. 3554-3582. See also:

Grain-size assessment of aggregates through image processing

Grain-size analysis of unconsolidated particles plays an important role in many areas of science and engineering including sedimentology, waste management, nanomaterials, corrosion and food engineering. The objective of this research is the development of image-based methods for computing grey-scale granulometries and estimating the mean size of fine and coarse aggregates. We are currently investugating the use of area morphology and combined information from both openings and closings to determine the size distribution.

In the picture: different types of aggregates sorted by increasing order of size. From top-left, in column-wise order: very coarse sand, very fine pebbles, fine pebbles, medium pebbles and coarse pebbles. From: Bianconi, F. et al. Grain-size assessment of fine and coarse aggregates through bipolar area morphology (2015) Machine Vision and Applications, 26 (6), pp. 775-789. See also:

Detection of coral reefs from sidescan sonar imagery

Sidescan sonar is commonly used to capture high-resolution acoustic imagery of the seabed. Different seabed regimes and habitats produce textural signatures recognisable to a human interpreter. Discriminating a particular habitat from the background can be thought of as a spatially distributed target recognition problem. We are currently investigating the effectiveness of texture analysis methods in this context.

From top-left, in column-wise order: bedforms, boulders, mussels, coral reef (Sabellaria Spinulosa) and sand. From: Harrison, R. et al. A texture analysis approach to identifying sabellaria spinulosa colonies in sidescan sonar imagery (2011) Proceedings - 2011 Irish Machine Vision and Image Processing Conference, IMVIP 2011, art. no. 6167881, pp. 58-63.