Agricultural Land cover mapping by active learning from multispectral spot-7 satellite image - Archive ouverte HAL
Poster De Conférence Année : 2016

Agricultural Land cover mapping by active learning from multispectral spot-7 satellite image

Résumé

Agricultural practices are major drivers of water flows in cultivated landscapes. Land Cover mapping have a strong impact onto run off and soil erosion at the landscape and watershed scales. In practice, there is a need for agricultural land cover mapping at both scales that could be integrated into hydrological models to better understand the considered hydrological behavior and thus improve water resources management Field methods are inadequate for characterizing the spatial variability of crops at such scales. Remote sensing appears therefore as a promising alternative. The classification of agricultural land cover using a single multispectral satellite images has proven to be challenging due to the high parcel intra variability over a wide study area. Our objective is land cover mapping of agricultural fields, at a large scale, using active learning techniques. The methods are applied to a spot-7 multispectral satellite image over a 35 km2 area of Lebna catchment in the North Eastern Tunisia. The image high spatial resolution allows a large scale land cover mapping on a wide extent. The proposed method is based on a supervised classification, using field observations as learning samples. The first difficulty consists is having a very small number of learning samples. The classifier model is then constructed locally, making it suboptimal for the entire area. Indeed, the learning samples are assumed to be representative of the whole data set which is rarely confirmed in practice on a wide area. To solve this issue, the proposed method is based on active learning techniques that build an efficient learning set by improving iteratively the model performance by adding the most informative samples. Samples are then labelled by the user and allow to construct a new classifier model that should be more efficient. The selection of new samples needs a strategy to rank pixels. Two criteria are used and often coupled: uncertainty and diversity. The samples should be the most informative (I.e uncertain for the current classifier model) and diverse (I.e non redundant). The used uncertainty measure is based on a random forest classifier probability. This measure can be applied to any other classifier that provides a probability of belonging to different classes. For sample diversity, two metrics are used; a similarity-based and clustering-based techniques. Besides, this paper focuses on an operational strategy that allows mapping agriculture cover on a large wide area. Two schemes are possible; either a pixel-based classification or a parcel- based classification. The latter assumes having a digitized parcels; using GIS techniques, of the study area but has the advantage of highly reducing computing times and leading to a land cover map at the parcel scale that can be directly used in hydrological models. First results show the effectiveness of active learning techniques to map a wide area while maintaining a small number of training samples.
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Dates et versions

hal-01390037 , version 1 (31-10-2016)

Identifiants

  • HAL Id : hal-01390037 , version 1

Citer

Ines Ben Slimene Ben Amor, Nesrine Chehata, Philippe Lagacherie, Jean-Stéphane Bailly, Imed Riadh Farah. Agricultural Land cover mapping by active learning from multispectral spot-7 satellite image. International Conference & Exhibition Advanced Geospatial Science \& Technology (TeanGeo 2016), 18-20 October 2016, Tunis, Tunisia, Oct 2016, Tunis, Tunisia. , 2016. ⟨hal-01390037⟩
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