Use of dense time series of high resolution
images for change detection
and land use classification
J. Inglada, B. Beguet, J.-F. Dejoux, C. Marais-Sicre, D. Ducrot, M. Huc,
O. Hagolle, F. Baup, G. Dedieu
CESBIO, UMR 5126, Toulouse, France - firstname.lastname@example.org
Real Time Land Cover Map Production
CESBIO’s Sud-Ouest Project: the goal of this project is to contribute to the understanding and the modeling of the continental surfaces at the landscape and regional levels and to
increase knowledge and develop generic methods. Yearly monitoring and regular satellite image acquisitions since 2006, 3 permanent instrumented sites.
Land-cover map production: as a means (input for models) but also as a research goal in itself.
Real time land cover map production: know as soon as possible in the agricultural season which is the crop which is going to be grown.
Formosat-2: 11 images in 2008.
• For soil work:
– 29th August to 12th November 2008
– 5 Formosat-2 images
Image pre-processing: all data are geometrically corrected and ra-
diometrically calibrated; cloud screening is performed 
(a) February 11, 2008 (b) July 10, 2008 (c) October 26, 2008
• 14 terrain surveys
• 650 plots revisited
• only 501 plots were kept for the land-cover classiﬁcations
• accuracy of the surveys allowing for diachronic studies
Soil work: 7 ﬁeld surveys for 300 plots
• Each ground sample is associated
with a conﬁdence index
• Some soil states are visible on the
ground before being detected on
Yearly classiﬁcation: using the
data for the whole season.
Advanced methods: described
in [2, 4]
class accuracy (%)
broad leaf forest 97.88
needle leaf forest 97.05
Main goal: improve real-time crop classiﬁcation; soil work can give hints on the type of crop
Soil map: is also interesting in itself as a product
Classes of interest:
Inter-crop Stubble disking Deep ploughing Harrowing Sowing preparation Emergence
Crops (C): Sunﬂowers, which are mostly dry in September and harvested in September or October. Irrigated
soybean and maize, which are green in September and begin to dry in October (harvest in October and
Inter-crop (IC): Begin after harvest. No recent or visible soil tillage. Stubble stands often right, crop
residues may be visible on top soil. Some green plants can grow, like volunteers (or regrow) and weeds, if
climatic conditions are favorable (rain, etc.).
Stubble disking (SD): Superﬁcial (5 to 15 cm) soil tillage in order to mix crop residues and soil and to
destroy green vegetation (weeds). Soil surface is irregular, has some small clods and a small roughness.
Stubble and crop residues are partly visible.
Deep ploughing (DP): Mainly mouldboard ploughing between 20 to 45 cm deep. More than 95% bare
soil: no visible crop residues. Visible clods and strong roughness.
Harrowing (H): Secondary or superﬁcial tillage. More than 95% bare soil. There are medium sized clods.
Improper for seedling. Various tillage operations are possible: rotary harrowing, chiseling, superﬁcial plough-
ing (less than 20 cm deep).
Remark: Some green plants (volunteers, weeds) may be visible for the 4 previous categories, only if climatic
conditions are favorable and duration between each stage or soil tillage is suﬃcient. In the present poster,
it was sometimes the case only in inter-crops or after stubble disking.
Sowing preparation (SP): More than 95% bare soil. Soil ready for seedling. Regular surface. Small clods.
Emergence (E): Germination. Plants are visible from ﬁeld borders and are at cotyledons or ﬁrst leaves
development stages. Plant height lower than 5 cm.
Radiometry only: only the reﬂectances and combi-
nations of them (indexes) are used; no texture, statis-
tics, nor object-based features.
Statistical analysis: the temporal evolution of the
reﬂectances and the indexes – globally and per class
– are studied.
2 kinds of analysis:
1. Identiﬁcation of the soil state: classiﬁcation
2. Identiﬁcation of the transitions between states:
SVM classiﬁcation: Support Vector Machines 
are both used as separability measure and as clas-
G2 + R2 + NIR2
Direct approach: each soil state is
considered a class and a supervised
classiﬁcation is performed
Crop class: not so easy to classify,
since it corresponds to several crop
• IC can be confused with MT, since
the amount of green vegetation
before tilling varies very much;
• many confusions between bare soil
• germination is correctly detected
Grouping soil classes improves the
C IC SD DP H SP E
C 66.4 9.54 7.08 4.67 0.35 7.53 4.43
IC 6.54 64.67 14.14 0.95 4.71 3.89 5.1
S 4.08 6.6 63.5 1.5 13.6 6.94 3.78
DP 6.36 2.76 2.64 57.54 16.51 10.53 3.66
H 1.53 1.35 6.9 20.85 44.09 23.17 2.11
SP 3.6 0.0 6.17 23.1 13.12 41.52 12.49
E 1.28 5.85 1.67 0.08 1.72 1.6 87.8
Overall Accuracy = 0.6085
Kappa = 0.541
C IC SD Soil E
C 65.65 10.47 8.9 7.12 7.86
IC 6.19 65.22 16.16 6.23 6.2
SD 5.29 6.61 67.88 15.43 4.79
Soil 3.92 2.16 8.18 77.27 8.47
E 2.98 6.39 2.32 2.31 86.0
Overall Accuracy = 0.7235
Kappa = 0.6555
Classes are transitions: supervised classiﬁcation is used in order to detect transitions between soil states.
C→ IC SD DP H SP
IC 73.49 16.23 0.03 1.07 9.18
SD 6.93 53.17 5.63 12.73 21.54
DP 0.65 2.14 83.07 3.54 10.6
H 2.47 5.93 10.7 74.97 5.93
SP 5.08 1.63 8.96 2.5 81.83
Overall Accuracy = 0.7354
Kappa = 0.667
IC→ SD DP H SP
SD 75.99 11.54 10.97 1.5
DP 3.58 89.67 6.75 0.0
H 14.32 15.87 62.15 7.66
SP 0.58 0.0 2.92 96.5
Overall Accuracy = 0.8125
Kappa = 0.7485
SD→ DP H SP E
DP 74.1 14.02 3.2 8.68
H 23.96 32.88 28.59 14.57
SP 7.51 13.69 65.91 12.89
E 6.82 5.69 7.25 80.24
Overall Accuracy = 0.633
Kappa = 0.5105
Transition D→H H→SP H→E SP→E
Accuracy (%) 97.0 88.74 87.91 96.76
• The number of transitions is very low for some
cases (between 12 and 50 plots; or between 1000
and 10000 pixels)
• Many transitions between states can’t be de-
• However, some changes are well detected (about
90% and more)
Soil work knowledge is needed to improve real-time land-cover map production; soil maps
are also useful in themselves
Soil states are diﬃcult to identify using direct classiﬁcation and optical radiometry only
Soil state changes can be detected in some cases, but many transitions seem diﬃcult to identify
 C.J.C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998.
 D. Ducrot, A. Masse, C. Marais-Sicre, J-F. Dejoux, and F. Baup. Multisensor and multitemporal image fusion methods to improve remote sensing image classiﬁcation. In Recent Advances in Quantitative Remote
Sensing, September 2010.
 O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images. Remote Sensing of Environment,
114:1747–1755, August 2010.
 S. Idbraim and D. Ducrot. An unsupervised classiﬁcation using a novel ICM method with constraints for land cover mapping from remote sensing imagery. International Review on Computers and Software (IRECOS),