Geographical Area Mapping

Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
IEEE Conference Record # 42487; IEEE Xplore ISBN:978-1-5386-0965-1
Geographical Area Mapping and Classification
Utilizing Multispectral Satellite Imagery Processing
Based On Machine Learning Algorithms
Classifying Land based on its use for different purposes
Apurva Saksena, Anushka Ringshia, Arnnava Sharma, Aparna Halbe
[email protected], [email protected], [email protected], [email protected]
Sardar Patel Institute of Technology, Andheri, Mumbai
Abstract—Geographically, a city is characterized as a
patchwork of intensive land-uses. Land-use is the rational and
judicious approach of allocating available land resources for
different activities (such as settlements, arable fields, pastures,
and managed woods) within a city. It is a way of utilizing the
land, including the allocation, planning, and management of its
resources. The use of a particular patch of land and its physical
character are linked. However, research that establishes this link
is lacking despite the proliferation of geospatial data. Linking a
city's physical form with its function is the goal of this paper.
Keywords—Land-cover; land-use; sustainability; development;
remote sensing; mapping;
I.
INTRODUCTION
Any global city cannot be understood without reference to
its spatial forms such as commercial, residential, industrial,
marshes/lakes, defence and more.[1]
Hence, our goal is to use remote sensing to analyze
environment variables like vegetation, impervious surfaces
and soil; encoding them into numerical categories and
classifying, to finally link a city’s physical form with its
functions. The aim is to ensure the highest and best use of the
land resources by promoting more efficient utilization,
acquisition, and disposition of land.[2]
land cover. Therefore, quantifying these land resources and
mapping them to measure current situations and how they are
changing is critical.[5]
There are several types of land uses, namely:
●
●
●
●
Residential- includes housing area
Commercial - for businesses and factories
Defence
Recreational - comprising of fun and non-essentials
like gardens and parks, tourism
● Transit- roads and highways, railways, airports and
even seaways
● Agricultural - arable farmlands and pastures
● Managed woods
● Mining refineries- for coal, petroleum, electricity
generation and more
Land use shows how people use the landscape – whether
for development, conservation, or mixed uses. The different
types of land cover can be managed or used quite
differently.[6]
Land cover: Land cover is the physical material at the
surface of the earth. It comprises of vegetation and resources
like grass, asphalt, trees, bare ground, water, etc. Land cover
data basically documents how much of a region is covered by
forests, wetlands, impervious surfaces, agriculture, and other
land and water types (including wetlands or open water) [3].
By analyzing satellite and aerial imagery, the land cover can
be determined. Identification, delineation and mapping of this
land cover establish the baseline from which global
monitoring activities like change detection, further studies,
resource management, and planning activities can take place.
The land cover also provides the ground cover information for
baseline thematic maps.[4]
Land use: Land use is a set of functions that can be
applied to the land available to them. Its practices have a
significant impact on the natural resources such as soil, water
and vegetation. Deforestation of the temperate regions, urban
sprawl, soil erosion or degradation, salinization, and
desertification are some of the major effects of land use on
Fig.1.
Overview of Land cover to Land Use (Images show Landsat
imagery, Overlay point grid, Image interpretation and Land use to Land cover
maps)
978-1-5386-0965-1/18/$31.00 ©2018 IEEE
1065
Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
IEEE Conference Record # 42487; IEEE Xplore ISBN:978-1-5386-0965-1
II.
METHODOLOGY
The uniqueness of land-use to particular locations can be
exploited by combining human expertise with the advantages
offered by machine learning algorithms. The assumption
behind this method is that land-use can be modelled in terms
of environmental variables: vegetation, impervious surfaces
and soil (VIS) . Urban ecosystems are a composite of these
three variables and therefore can be observed, quantified and
measured from satellite images.[7] In this project, the
morphology of impervious surfaces will be measured and
characterized per land-use category. Impervious and pervious
(vegetation and soil) surfaces can be encoded into numerical
categories and classified using machine learning algorithms.
In this project, the VIS can be modelled by taking advantage
of the linear correlation of impervious and pervious surfaces in
very high resolution (0.5mx0.5m pixels) and medium
resolution (30mx30m pixels) satellite images. Impervious
surfaces can then be further characterized according to their
morphology within arbitrarily defined land-use boundaries and
classified into land-use categories. [8]
A. Pre-processed Satellite Images
A Landsat image is a satellite image. The Landsat program
was started for the primary reason of obtaining a global
archive of satellite images[9] .WorldView-2 is a commercial
Earth
observation
satellite.
WorldView-2
provides
commercially available panchromatic imagery of 0.46 m
(18in) resolution and eight-band multispectral imagery with
1.84 m (72 in) resolution.[4] These images are then preprocessed to give an intensity image. Intensity images are
required to prevent unwanted distortions.The WorldView-2
image has a better resolution and hence it is used for landcover classification as well [10]. The preprocessed image
undergoes classification using Support-Vector-Machine
Algorithm in the Spatial Analyst toolbox of the ArcGIS
software.[11] The land-cover map is hence obtained which
will eventually lead to the land-use map.
B. Computing the NDVI index
The normalized difference vegetation index (NDVI) is a
simple graphical indicator that can be used to analyze remote
sensing measurements, and assess whether the target being
observed contains live green vegetation or not.[12]
Live green plants absorb solar radiation in the photo
synthetically active radiation (PAR) spectral region, which
they use as a source of energy in the process of
photosynthesis. Leaf cells have also evolved to re-emit solar
radiation in the near-infrared spectral region because the
photon energy at wavelengths longer than about 700
nanometers is not large enough to synthesize organic
molecules. The pigment in plant leaves, chlorophyll, strongly
absorbs visible light (from 0.4 to 0.7 µm) for use in
photosynthesis. The cell structure of the leaves, on the other
hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm).
The more leaves a plant has, the more these wavelengths of
light are affected, respectively. It was thus possible to exploit
the strong differences in plant reflectance to determine their
spatial distribution in these satellite images [13] .Red and NIR
stand for the spectral reflectance measurements acquired in the
red (visible) and near-infrared regions, respectively. Using this
index, a certain area on the map can be concluded to be
suitable for agriculture.[14]
Fig.2.
General Flowchart
Negative values of NDVI (values approaching -1)
correspond to water. Values close to zero (-0.1 to 0.1)
generally correspond to barren areas of rock, sand, or snow.
Lastly, low, positive values represent shrub and grassland
(approximately 0.2 to 0.4), while high values indicate
temperate and tropical rainforests (values approaching 1).[15]
978-1-5386-0965-1/18/$31.00 ©2018 IEEE
1066
Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
IEEE Conference Record # 42487; IEEE Xplore ISBN:978-1-5386-0965-1
Fig.3.
The green regions indicate spaces having NDVI index between 0
to 1 whereas the blue regions have NDVI index between -1 to 0
In this way, the NDVI index for both Landsat and
WorldView-2 images is computed. We hence obtain an
intensity map containing the pixelized version of the map
showing areas with different NDVI index.The difference
between the Landsat and WorldView-2 is that of resolution.
The WorldView-2 has a better and deeper resolution of 0.5m
while Landsat images provide a resolution of 30m.
C. Image Masking
ArcGIS Software is used for image masking.The Image
Masking tool takes an input image, masks it, and produces a
new image which is a copy of the input image, except that the
new image will have its pixel intensity value set to zero (or
some other chosen background intensity value) according to
the mask and the masking operations performed [16]. Hence,
it can be used for sharpening, blurring, embossing or detection
of an edge. Here the input is the satellite image and after
masking, we should get a clear intensity diagram.[17].
D. Encoding into numerical categories
Impervious and pervious (vegetation and soil) surfaces can
be encoded into numerical categories and classified using
machine learning algorithms. Urban ecosystems are an
agglomeration of these three fundamental variables (V-I-S)
and therefore can be observed, quantified and measured from
satellite images.The basic idea is to encode into numerical
categories, upon which machine learning algorithms can then
be applied to classify.[18]
Fig.4.
Vegetation impervious surface-soil model
E. Classifying using Support-Vector-Machine Algorithm
The Support-Vector-Machine algorithm is used to classify
in the Spatial Analyst Toolbox of ArcGIS software version
10.3.1. [11] The working of any machine learning algorithm
depends on the training data set. The initial input on the basis
of which a machine can learn is crucial for the success of any
machine learning algorithm. We classify land into a cloud,
water, white roofs, blue roofs, green roofs, dark green roofs,
light red roofs, brown roofs, light grey surface, dark grey
surface, bare soil, grassland, tree canopy, shadow and
farmland.[19] Whenever any satellite image is input, it will be
classified into these categories as shown in the fig.6 [20].
978-1-5386-0965-1/18/$31.00 ©2018 IEEE
1067
Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
IEEE Conference Record # 42487; IEEE Xplore ISBN:978-1-5386-0965-1
Fig.8.
Land Use category
III.
Fig.5.
Preliminary 2015 Land-Cover Map of a city
Fig.6.
Land Cover Classes
OBSERVATION AND RESULTS
Here we have used Pre-Processed Landsat And
WorldView-2 images. After following the above-given
methodology we can observe them from a raw satellite image
we can obtain a Land-Cover map using Support-MachineVector Algorithm [10]. We have classified land based the
cover of the land like blue, yellow, green covers but our main
aim of this paper is to obtain a Land-Use [9]. For this, we have
used image masking on the Land-Cover map into categories
like agricultural, commercial, industrial, mixed-use and finally
residential represented by colours green, red, violet, ochre and
yellow respectively. So we can observe how a raw satellite
image has been converted to a Land-Use map which can help
us classify land according to its use.[12]
F. Land Use map
The final output displaying the land-use is obtained by
masking the land-cover map into categories like agricultural,
commercial, industrial, mixed-use and finally residential. The
respective colour codes are green, red, violet, ochre and
yellow [21]. By displaying the land-use map, the utility of
various areas is revealed and a rough estimate of the soil
condition is obtained. This method is found to be accurate and
reliable. By learning from the training data set, all input
satellite images can be effectively converted to land-use
maps[22].
A. Land-Use Classification Accuracy
TABLE 1.
Fig.9.
Accuracy report 1
After performing the Land-Use classification we need to
check for its accuracy to see if it is viable. The sum total of the
values in the rows gives the total number of retrieved
instances. The value in the column gives the number of
relevant instances. So for example, the total number of
retrieved instances for commercial are 4 and the relevant
instances are just 2. However, the number of correctly
retrieved relevant instances is just 1 as there might be 2 times
when commercial has been detected but it was corrected
detected just once. [14]
Fig.7.
Output of the same city is the Land Use map
978-1-5386-0965-1/18/$31.00 ©2018 IEEE
1068
Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
IEEE Conference Record # 42487; IEEE Xplore ISBN:978-1-5386-0965-1
unfair distribution of economic assets, and the loss of
community consciousness. Increasing the sustainability of
communities will require a shift from poorly managed sprawl
to land use planning that can build and keep up efficient
Infrastructure, encourage close-knit neighborhoods and
community consciousness, and preserve the environment.
TABLE 2.
V.
Fig.10.
The accuracy of the Land-Use Map Classification should be
around 90 percent ideally to get near perfect results. Therefore,
more efficient and accurate classification algorithms need to be
developed in order to increase the accuracy of the Land-Use
Map Classification. Also, better image processing software
could be developed and used in order to correctly capture and
process a geospatial or a hyperspectral image.
Accuracy report 2
Precision is the fraction of correct relevant instances among
the total number of retrieved instances. A recall is the fraction
of correct relevant instances that have been retrieved by the
total number of relevant instances. Support is the total number
of relevant instances.
The F1 score is a measure of a test's accuracy. It considers
both the precision and the recall of the test to compute the
score.[20]
So for example, Precision for Commercial is ¼=0.25 as a
number of correct relevant instances are 1 and total instances
retrieved are 4. The Recall for Commercial is ½=0.50 as a
number of correct relevant instances are 1 and number of
relevant instances are 2. The using the above formula we can
find the F1 score. Support is 2 as the number of relevant
instances is 2.
Similarly, we do the same for the rest and find the average
precision, recall, F1 score and support which is 0.71, 0.60,
0.62 and 20 respectively. This helps us in knowing that the
Land-Use Map that we have obtained by masking the LandCover Map gives us fairly accurate results. This will help us
correctly classify land based on its use. [23]
IV.
FUTURE WORK
VI.
The authors gratefully acknowledge the contributions of the
entire I.T. department and Sardar Patel Institute of Technology
along with its staff for their work.
REFERENCES
[1]
O. Arino, P. Bicheron, F. Achard, J. Latham, R. Witt, J.-L. Weber,
"GLOBCOVER—The most detailed portrait of Earth", European Space
Agency ESA Bulletin, pp. 24-31, 2008
[2]
M. C. Hansen, T. R. Loveland, "A review of large area monitoring of
land cover change using Landsat data", Remote Sens. Environ., 2012.
[3]
Yang, Yi, and Shawn Newsam. "Geographic image retrieval using local
invariant features." IEEE Transactions on Geoscience and Remote
Sensing51.2 (2013): 818-832.
[4]
Comber, A., Fisher, P., & Wadsworth, R. (2005). What is land cover?
Environment and Planning B: Planning and Design, 32(2), 199–209.
https://doi.org/10.1068/b31135
[5]
Cihlar, J., & Jansen, L. (2001). From Land Cover to Land Use: A
Methodology for Efficient Land Use Mapping over Large Areas. The
Professional Geographer, 53(2), 275–289. https://doi.org/10.1111/00330124.00285
[6]
D. Hoiem, A. A. Efros, M. Hebert, "Recovering surface layout from an
image", Int. J. Comput. Vis., vol. 75, no. 1, pp. 151-172, Oct. 2007.
[7]
J. Feranec, G. Hazeu, S. Christensen, G. Jaffrain, "Corine land cover
change detection in Europe (case studies of the Netherlands and
Slovakia)", Land Use Policy, vol. 24, pp. 234-247, 2007.
[8]
RIDD, M. K. (1995). Exploring a V-I-S (vegetation-impervious surfacesoil) model for urban ecosystem analysis through remote sensing:
comparative anatomy for cities†. International Journal of Remote
Sensing,
16(12),
2165–2185.
https://doi.org/10.1080/01431169508954549
[9]
J. Knorn, A. Rabe, V. C. Radeloff, T. Kuemmerle, J. Kozak, P. Hostert,
"Land cover mapping of large areas using chain classification of
neighboring Landsat satellite images", Remote Sens. Environ., vol. 113,
pp. 957-964, 2009.
CONCLUSION
The ways in which municipalities, states, and nations plan
the physical arrangement or land use of our communities is
critical to sustainability. The land use patterns, which are
shared by cities across the world have given rise to complex
problems created by urban sprawl faced by all—growing
traffic congestion and lengthening commute times, air
pollution, wasteful energy consumption and greater reliance
on petroleum, elimination of open space and wildlife habitat,
ACKNOWLEDGMENT
[10] W. B. Cohen, S. N. Goward, "Landsat's role in ecological applications of
remote sensing", Bioscience, vol. 54, pp. 535-545, 2004.
978-1-5386-0965-1/18/$31.00 ©2018 IEEE
1069
Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
IEEE Conference Record # 42487; IEEE Xplore ISBN:978-1-5386-0965-1
[11] Griffiths, Patrick, et al. "A pixel-based Landsat compositing algorithm
for large area land cover mapping." IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing 6.5 (2013): 2088-2101.
[12] M. Baumann, M. Ozdogan, T. Kuemmerle, K. J. Wendland, E. Esipova,
V. C. Radeloff, "Using the Landsat record to detect forest-cover changes
during and after the collapse of the Soviet Union in the temperate zone
of European Russia", Remote Sens. Environ., vol. 124, pp. 174-184,
2012..
[13] J. C.-W. Chan, D. Paelinckx, "Evaluation of random forest and adaboost
tree-based ensemble classification and spectral band selection for
ecotope mapping using airborne hyperspectral imagery", Remote Sens.
Environ., vol. 112, no. 6, pp. 2999-3011, Jun. 2008.
[14] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical
Learning, New York:Springer-Verlag, 2009.
[15] C. E. Woodcock, S. A. Macomber, M. Pax-Lenney, W. B. Cohen,
"Monitoring large areas for forest change using Landsat: Generalization
across space time and Landsat sensors", Remote Sens. Environ., vol. 78,
pp. 194-203, 2001.
[16] Senthilnath, J., et al. "Hierarchical clustering algorithm for land cover
mapping using satellite images." IEEE journal of selected topics in
applied earth observations and remote sensing 5.3 (2012): 762-768.
[17] S. Z. Li, Markov Random Field Modeling in Image Analysis, New
York:Springer-Verlag, 2009.
[18] C. Tomasi, R. Manduchi, "Bilateral filtering for gray and color images",
Proc. 6th Int. Conf. Computer Vis., pp. 839-846, 1998.
[19] T. M. Lillesand, R. W. Kiefer, J. W. Chipman, Remote Sensing and
Image Interpretation, NJ, Hoboken:Wiley, 2003.
[20] Schindler, Konrad. "An overview and comparison of smooth labeling
methods for land-cover classification." IEEE transactions on geoscience
and remote sensing 50.11 (2012): 4534-4545.
[21] P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, E. Lambin, "Digital
change detection methods in ecosystem monitoring: A review", Int. J.
Remote Sens., vol. 25, pp. 1565-1596, 2004.
[22] B. Sirmaek, C. Unsalan, "Urban-area and building detection using sift
keypoints and graph theory", IEEE Trans. Geosci. Remote Sens., vol.
47, no. 4, pp. 1156-1167, Apr. 2009.
[23] Storper, M., & Scott, A. J. (2016). Current debates in urban theory: A
critical
assessment.
Urban
Studies,
53(6),
1114–1136.
https://doi.org/10.1177/0042098016634002
978-1-5386-0965-1/18/$31.00 ©2018 IEEE
1070