VIETNAM NATIONAL UNIVERSITY, HANOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY
MAN DUC CHUC
RESEARCH ON LAND-COVER CLASSIFICATION
METHODOLOGIES FOR OPTICAL SATELLITE IMAGES
MASTER THESIS IN COMPUTER SCIENCE
Hanoi – 2017
VIETNAM NATIONAL UNIVERSITY, HANOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY
MAN DUC CHUC
RESEARCH ON LAND-COVER CLASSIFICATION
METHODOLOGIES FOR OPTICAL SATELLITE IMAGES
DEPARTMENT: COMPUTER SCIENCE
MAJOR: COMPUTER SCIENCE
CODE: 60480101
MASTER THESIS IN COMPUTER SCIENCE
SUPERVISOR: Dr. NGUYEN THI NHAT THANH
Hanoi – 2017
PLEDGE
I hereby undertake that the content of the thesis: “Research on Land-
Cover classification methodologies for optical satellite images” is the research
I have conducted under the supervision of Dr. Nguyen Thi Nhat Thanh. In the
whole content of the dissertation, what is presented is what I learned and
developed from the previous studies. All of the references are legible and legally
quoted.
I am responsible for my assurance.
Hanoi, day month year 2017
Thesis’s author
Man Duc Chuc
ACKNOWLEDGEMENTS
I would like to express my deep gratitude to my supervisor, Dr. Nguyen Thi
Nhat Thanh. She has given me the opportunity to pursue research in my favorite
field. During the dissertation, she has given me valuable suggestions on the
subject, and useful advices so that I could finish my dissertation.
I also sincerely thank the lecturers in the Faculty of Information
Technology, University of Engineering and Technology – Vietnam National
University Hanoi, and FIMO Center for teaching me valuable knowledge and
experience during my research.
Finally, I would like to thank my family, my friends, and those who have
supported and encouraged me.
This work was supported by the Space Technology Program of Vietnam
under Grant VT-UD/06/16-20.
Hanoi, day month year 2017
Man Duc Chuc
1
Content
CHAPTER 1. INTRODUCTION
…………………………………………………………………………
5
1.1. Motivation
…………………………………………………………………………………………….
5
1.2. Objectives, contributions and thesis structure ……………………………………………
9
CHAPTER 2. THEORETICAL BACKGROUND ……………………………………………….
10
2.1. Remote sensing concepts ………………………………………………………………………
10
2.1.1.
General introduction ……………………………………………………………………
10
2.1.2.
Classification of remote sensing systems ……………………………………….
12
2.1.3.
Typical spectrum used in remote sensing systems …………………………..
14
2.2. Satellite images ……………………………………………………………………………………
15
2.2.1.
Introduction
………………………………………………………………………………..
15
2.2.2.
Landsat 8 images ………………………………………………………………………..
17
2.3. Compositing methods …………………………………………………………………………..
20
2.4. Machine learning methods in land cover study ………………………………………..
21
2.4.1.
Logistic Regression……………………………………………………………………..
21
2.4.2.
Support Vector Machine
………………………………………………………………
22
2.4.3.
Artificial Neural Network
…………………………………………………………….
23
2.4.4.
eXtreme Gradient Boosting ………………………………………………………….
25
2.4.5.
Ensemble methods ………………………………………………………………………
25
2.4.6.
Other promising methods …………………………………………………………….
26
CHAPTER 3. PROPOSED LAND COVER CLASSIFICATION METHOD ………….
27
3.1. Study area
……………………………………………………………………………………………
27
3.2. Data collection …………………………………………………………………………………….
28
3.2.1.
Reference data
…………………………………………………………………………….
28
2
3.2.2.
Landsat 8 SR data ……………………………………………………………………….
30
3.2.3.
Ancillary data
……………………………………………………………………………..
31
3.3. Proposed method
………………………………………………………………………………….
31
3.3.1.
Generation of composite images …………………………………………………..
32
3.3.2.
Land cover classification ……………………………………………………………..
34
3.4. Metrics for classification assessment
………………………………………………………
35
CHAPTER 4. EXPERIMENTS AND RESULTS ………………………………………………..
36
4.1. Compositing results ……………………………………………………………………………..
37
4.2. Assessment of land-cover classification based on point validation
……………..
38
4.2.1.
Yearly single composite classification versus yearly time-series
composite classification
……………………………………………………………………………..
38
4.2.2.
Improvement of ensemble model against single-classifier model
………
40
4.3. Assessment of land-cover classification results based on map validation ……
42
CHAPTER 5. CONCLUSION …………………………………………………………………………..
44
3
LIST OF TABLES
Table 1. Description of seven global land-cover datasets. ……………………………………….
7
Table 2. Some featured satellite images ………………………………………………………………
16
Table 3. Landsat 8 bands. ………………………………………………………………………………….
18
Table 4. Review of compositing methods for satellite images.
……………………………….
20
Table 5. Training and testing data. ……………………………………………………………………..
28
Table 6. Summary of Year score, DOY score, Opacity score and Distance to cloud/cloud
shadow for L8SR composition …………………………………………………………………………..
33
Table 7. F1 score, F1 score average, OA and kappa coefficient for 7 land cover classes
of six classification cases obtained using XGBoost. Best classification cases are written
in bold. ……………………………………………………………………………………………………………
39
Table 8. OA, kappa coefficient, F1 score average for each single-classifier and ensemble
model. Best classification cases are written in bold.
……………………………………………..
40
Table 9. Confusion matrix of ensemble model. ……………………………………………………
41
Table 10. Error (ha and %) of rice mapped area for different classification scenarios.
43
4
LIST OF FIGURES
Figure 1. Rice covers map of Mekong river delta, Vietnam in 2012. ………………………..
6
Figure 2. The acquisition of data in remote sensing.
……………………………………………..
11
Figure 3. Introduction of a typical remote sensing system.
…………………………………….
12
Figure 4. Passive (left) and active (right) remote sensing systems. …………………………
13
Figure 5. Geostationary satellite (left) and Polar orbital satellite (right). …………………
14
Figure 6. Typical wavelengths used in remote sensing.
…………………………………………
15
Figure 7. Landsat 8 images
………………………………………………………………………………..
17
Figure 8. Landsat 7 and Landsat 8 bands …………………………………………………………….
18
Figure 9. Comparison of Landsat 8 OLI (left) and SR (right) images. …………………….
19
Figure 10. An example of MLP.
…………………………………………………………………………
24
Figure 11. Hanoi city, study area of this study.
…………………………………………………….
28
Figure 12. Examples of experimental data shown in Google Earth, sampled points are
represented by while-colored squares over the Google Earth base images. ……………..
30
Figure 13. Landsat 8 footprints over Hanoi. ………………………………………………………..
30
Figure 14. Statistics of Landsat 8 SR images over Hanoi, (a) number of images by year
and month, (b) cloud coverage percentage per image
……………………………………………
31
Figure 15. Overall flowchart of the method …………………………………………………………
32
Figure 16. Clear observation count maps for each image used in the compositing process
(DOY 137, 169, 265, 281)
…………………………………………………………………………………
34
Figure 17. NDVI (above) and BSI (below) temporal profile of land-cover class
………
38
Figure 18. (a) Original surface reflectance images, (b) composite images, (c)
classification maps for each image, and (d) classified map obtained from time-series
composite images. ……………………………………………………………………………………………
39
Figure 19. F1 score for land-cover class obtained using multiple classifiers. …………..
41
Figure 20. 2016 Land-cover map for Hanoi based on the most accurate classification
using time-series composite imagery and the ensemble of five classifiers.
………………
42
5
CHAPTER 1. INTRODUCTION
In this chapter, I briefly present an introduction to remote sensing images and
its applications in different research areas. Furthermore, the problem of land cover
classification is also presented. Current progress and challenges in land cover
classification are discussed. Finally, motivations and problem statement of the research
are shown in the end of the chapter.
1.1.
Motivation
Remotely-sensed images have been used for a long time in both military and
civilization applications. The images could be collected from satellites, airborne
platforms or Unmanned Aerial Vehicles (UAVs). Among the three, satellite images have
gained popularity due to large coverage, available data and so on. In general, remotely-
sensed images store information about Earth object’s reflectance of lights, i.e. Sun’s
light in passive remote sensing [1]. Therefore, the images contain itself lots of valuable
information of the Earth’s surface or even under the surface.
Applications of remotely-sensed images are diverse. For example, satellite
images could be used in agriculture, forestry, geology, hydrology, sea ice, land cover
mapping, ocean and coastal [1]. In agriculture, two important tasks are crop type
mapping and crop monitoring. Crop type mapping is the process of identification crops
and its distribution over an area. This is the first step to crop monitoring which includes
crop yield estimation, crop condition assessment, and so on. To these aims, satellite
images are efficient and reliable means to derive the required information [1]. In
forestry, potential applications could be deforestation mapping, species identification
and forest fire mapping. In the forest where human access is restricted, satellite imagery
is an unique source of information for management and monitoring purposes. In
geology, satellite images could be used for structural mapping and terrain analysis. In
hydrology, some possible applications cloud be flood delineation and mapping, river
change detection, irrigation canal leakage detection, wetlands mapping and monitoring,
soil moisture monitoring, and a lot of other researches. Iceberg detection and tracking is
also done via satellite data. Furthermore, air pollution and meteorological monitoring
6
could be possible from satellite perspective. In general, many of the applications more
or less relate to land cover mapping, i.e. agriculture, flood mapping, forest mapping, sea
ice mapping, and so on.
Land cover (LC) is a term that refers to the material that lies above the surface
of the Earth. Some examples of land covers are: plants, buildings, water and clouds.
Land cover is the thing that reflects or radiates the Sun’s lights which then be captured
by the satellite’s sensors. Land use and land cover classification (LULCC) has been
considering as one of the most traditional and important applications in remote sensing
since LULCC products are essential for a variety of environmental applications [2].
Figure 1 shows a land cover map for Mekong river delta, Vietnam in 2012 derived from
MODIS images [3]. This map shows distribution of rice lands in the region.
Figure 1. Rice covers map of Mekong river delta, Vietnam in 2012.
Regarding land cover classification (LCC), there are currently many researches
around the world. These researches could be categorized by several criteria such as
geographical scale of classification, multiple land covers classification or single land
7
cover classification. For the former, LCC can be classified into regional or global
studies. Regional studies focus on investigating LCC methods for one or more specific
regions. Global studies concern classification at global scale. There are currently some
already published global land-cover datasets as presented in Table 1.
Table 1. Description of seven global land-cover datasets.
GLCC
UMD
GLC2000
MODIS
LC
GlobCove
r
GLCNM
O
FROM-
GLC
Sensor
AVHRR
AVHRR
SPOT-4
VEGETATI
ON
MODIS
MERIS
MODIS
LANDSA
T
Acquisitio
n time
04/1992–
03/1993
04/1992–
03/1993
11/1999–
12/2000
01/2001–
12/2002
12/2004 –
06/2006
01/2008 –
12/2008
01/2010 –
12/2010
Spatial
resolution
1 km
1 km
1 km
500 m
300 m
500 m
30 m
Input data
IGBP 1-km
AVHRR
10-day
composite,
DEM data,
Ecoregions
data,
Maps data.
41 metrics
derived
from
NDVI and
AVHRR
bands 1–5,
EROS
urban,
MODIS
water
mask
Daily
mosaics of 4
spectral
channels and
NDVI
of
SPOT,
JERS-1 and
ERS
radar
data,
DMSP data,
DEM
Monthly
MODIS
L2/L3
composite,
EOS
land/water
mask,
MODIS
16-day
EVI,
MODIS 8-
day DEM
MERIS
L1B data,
MERIS
mosaics
16-day
composite
of MODIS
2008 Data
MOD44W
and
SRTM
DEM
Landsat
TM/ETM
+
(30
meter),
MODIS
EVI time
series
(250
meter)
Bioclimati
c variables
(1km)
global
DEM
(1km)
Classificat
ion
method
Classificati
on
with
post-
classificati
on
refinement
Unsupervis
ed
Decision
tree
Unsupervise
d
classificatio
n
Decision
tree, Neural
networks
Unsupervi
sed
classificati
on
Combined
method of
supervised
classificati
on
and
individual
mapping
Maximum
likelihood
(MLC),
J4.8
Decision
tree,
Random
forests
and
Support
8
vector
machine
LC class
17 classes
14 classes
23 classes
17 classes
22 classes
20 classes
10 classes
Validation
data
Landsat
TM
and
SPOT
images
Other
digital
datasets
High
resolution
satellite data,
and ancillary
information
High
resolution
land cover
information
SPOT-
VEGETA
TION
NDVI, and
Virtual/Go
ogle Earth
Integrated
potential
map,
Google
Earth
image,
MODIS
images
MODIS
vegetatio,
DEM and
soil-water
condition
maps
Reported
accuracy
Globally
66.9%
Globally
69%
Globally
68.6 ± 5%
Globally
75%
Globally
67.1%
Globally
77.9%
Globally
64.9%
Although there are many efforts to map land covers globally, the LC accuracies are
still much lower than regional LC maps. This is understandable as there are many
challenges in LCC at global scale including diversity of land-cover types, lack of
ground-truth data, and so on [4]. In regional studies, the difficulties are more or less
reduced, thus resulting in more accurate LC maps. Some typical regional LC studies
could be mentioned, i.e. Hannes et al. investigated Landsat time series (2009 – 2012) for
separating cropland and pasture in a heterogeneous Brazilian savannah landscape using
random forest classifier and achieved and overall accuracy of 93% [5]. Xiaoping Zhang
et al. used Landsat data to monitor impervious surface dynamics at Zhoushan islands
from 2006 to 2011 and achieved overall accuracies of 86-88% [6]. Arvor et al. classified
five crops in the state of Mato Grosso, Brazil using MODIS EVI time series and their
OAs ranged from 74 – 85.5% [7].
Although land-cover classification (LCC) mapping at medium to high spatial
resolution is now easier due to availability of medium/high spatial resolution imagery
such as Landsat 5/7/8 [8], in cloud-prone areas, deriving high resolution LCC maps
from optical imagery is challenging because of infrequent satellite revisits and lack of
cloud-free data. This is even more pronounced in land cover with high temporal
dynamics, i.e. paddy rice or seasonal crops, which require observation of key growing
stages to correctly identify [9], [10]. Vietnam is located in a tropical monsoon climate
frequently covered by cloud [11], [12]. Some studies used high temporal resolution but
low spatial resolution images (MODIS) [13]. Some studies employed single-image
classifications [14]. However, common challenges of mono-temporal approaches
include misclassification between bare land or impervious surface and vegetation cover
type [15]. Whereas land cover classification using cloud-free Landsat scenes may lack
enough observations to capture temporal dynamics of land-cover types.
9
1.2.
Objectives, contributions and thesis structure
To date, land cover classification in cloud-prone areas is challenging. Furthermore,
efficient LC methods for the regions, especially for areas with high temporal dynamics
of land covers, are still limited. In this thesis, the aim is to propose a classification
method for cloud-prone areas with high temporal dynamics of land-cover types. It is
also the main contribution of the research to current development of land cover
classification. To assess its classification performance, the proposed method is first
tested in Hanoi, the capital city of Vietnam. Hanoi is one of the cloudiest areas on Earth
and has diverse land covers. In particular, the results of this thesis could be applicable
to other cloudy regions worldwide and to clearer ones also.
This thesis is organized into five chapters. In chapter 1, I give an introduction to
remotely-sensed data and its application in various domains. A problem statement is
also presented. Theoretical backgrounds in remote sensing, compositing methods and
land cover classification methods are introduced in Chapter 2. Proposed method is
presented in Chapter 3. Chapter 4 details experiments and results. Finally, some
conclusions of my thesis are drawn in Chapter 5.
10
CHAPTER 2. THEORETICAL BACKGROUND
This chapter reviews necessary concepts used in this thesis. Basic knowledge of
remote sensing science is presented in section 2.1. Section 2.2 introduces satellite
images and details of Landsat 8 data. Compositing methods for satellite images are
summarised in section 2.4. Finally, machine learning methods in land cover
classification are discussed in section 2.5.
2.1.
Remote sensing concepts
2.1.1. General introduction
Remote sensing is a science and art that acquires information about an object, an
area or a phenomenon through the analysis of material obtained by specialized devices.
These devices do not have a direct contact with the subject, area, or studied phenomena
(Figure 2) [1].
11
Figure 2. The acquisition of data in remote sensing1.
Electromagnetic waves that are reflected or radiated from an object are the main
source of information in remote sensing. A remote sensing image provides information
about the objects in form of radiated energy in recorded wavelengths. Measurements
and analyses of the spectral reflectance allow extraction of useful information of the
ground. Equipments used to sense the electromagnetic waves are called sensor. Sensors
are cameras or scanners mounted on carrying platforms. Platforms carrying sensors are
called carrier, which can be airplanes, balloons, shuttles, or satellites. Figure 1 shows a
typical scheme for remote sensing image acquisition. The main source of energy used
in remote sensing is solar radiation. The electromagnetic waves are sensed by the sensor
on the receiving carrier. Information about the reflected energy could be processed and
applied in many fields such as agriculture, forestry, geology, meteorology, environments
and so on.
A remote sensing system works in the following model: a beam of light, emitted by
the sun/the satellite itself, firstly reaches the Earth surface. It is then partially absorbed,
reflected and radiated back to the atmosphere. In the atmosphere, the beam may also be
1 http://tutor.nmmu.ac.za/uniGISRegisteredArea/intake13/Remote%20Sensing%20and%20GIS/sect2pr.pdf
12
absorbed, reflected or radiated for another time. On the sky, the satellite’s sensor will
pick up the beam that is reflected back to it. After that it is the process of transmitting,
receiving, processing and converting the radiated energy into image data. Finally,
interpretation and analysis of the image is done to apply in real-life applications. Figure
3 illustrates typical components of a remote sensing system [1].
Figure 3. Introduction of a typical remote sensing system.
Symbols:
– A: energy source.
– B: incoming source.
– C: the ground target.
– D: satellite.
– E: receiving system.
– F: image analysis system.
– G: application system.
2.1.2. Classification of remote sensing systems
Remote sensing systems can be classified by following criterias: energy source,
satellite’s orbit, spectrum of the receiver, etc [1].
Classification based on energy source: passive and active remote sensing systems
(Figure 4).
13
Figure 4. Passive (left) and active (right) remote sensing systems.
– Active remote sensing system: the source energy is the light emitted by an
artificial device, usually the transmitter placed on the flying equipment.
– Passive remote sensing system: the source energy is the Sun’s light.
Classification based on orbit (Figure 5):
– Geostationary satellite: is a satellite with a rotational speed equal to the
rotational speed of the earth. Relative position of the satellite as compared to
the earth is stationary.
– Polar orbital satellite: is a satellite with orbital plane which is perpendicular
or near perpendicular to the equatorial plane of the earth. The satellite’s
rotation speed is different from the rotation speed of the earth. It is designed
so that the recording time on a particular region is the same as the local time.
And the revisit time for a particular satellite is also fixed. For example,
Landsat 8 has a revisit time of 16 days2.
2 https://landsat.usgs.gov/landsat‐8
14
Figure 5. Geostationary satellite (left) and Polar orbital satellite (right).
Classification by receiving spectrum: visible spectrum, thermal infrared,
microwave,….
The sun is the main source of energy for remote sensing in visible and infrared bands.
Earth surface objects can also emit their energy in thermal infrared spectrum.
Microwave remote sensing uses ultra-high frequency radiation with a wavelength of one
to several centimeters. The energy used for active remote sensing is actively generated
from the transmitter. Radar technology is a type of active remote sensing. Active radar
emits energy to objects, then captures the radiation which is scattered or reflected from
the object.
2.1.3. Typical spectrum used in remote sensing systems
In fact, there are many different types of light. However, only a few spectral bands
are used in remote sensing (Figure 6). The following are frequently used.
– Visible light: are lights whose wavelengths are between 0.4 and 0.76 microns.
The energy provided by these wave bands plays an important role in remote
sensing.
– Near Infrared: are lights whose wavelengths are between 0.77 and 1.34
microns.
– Middle Infrared: are lights whose wavelengths are between 1.55 and 2.4
microns.
15
Figure 6. Typical wavelengths used in remote sensing3.
– Thermal Infrared: are lights whose wavelengths are between 3 and 22
microns.
– Microwave: are lights whose wavelengths are between 1 and 30 microns.
Atmosphere does not strongly absorb wavelengths greater than 2 centimeters
which allows day and night energy intake, without the effects of clouds, fog
or rain.
2.2.
Satellite images
2.2.1. Introduction
Satellite images are images of Earth or other planets collected by observation
satellites. The satellites are often operated by governmental agencies or businesses
around the world. There are currently many Earth observation satellites and they have
common characteristics including spatial resolution, spectral resolution, radiometric
resolution and temporal resolution. A detailed description of each resolution is shown
below [1].
– Spatial resolution: refers to the instantaneous field of view (IFOV) which is
the area on the ground viewed by the satellite’s sensor. For example, the
Landsat 8 satellite has 30-meter spatial resolution which means that a Landsat
8’s pixel covers an area on the Earth’s surface of 30m x 30m.
– Spectral resoalution: spectral resolution describes the ability of the sensor to
3 http://www.remote‐sensing.net/concepts.html
16
receive the Sun’s light. If conventional cameras on the phone can only obtain
wavelengths in the visible range including red, green and blue lights, many
satellite sensors have possibility to sense many other wavelengths such as
near infrared, short-wave infrared, and so on. For example, the TIRS sensor
mounted on Landsat 8 satellite can receive wavelengths ranging from 10.6 to
12.51 micrometers.
– Radiometric resolution: the radiometric resolution of a sensor describes the
ability to distinguish very small differences in light energy. A better
radiometric resolution can detect small differences in reflection or energy
output.
– Temporal resolution: temporal resolution of a satellite is the time interval
between two successive observations over the same area on the Earth’s
surface. For example, the temporal resolution of Landsat 8 satellite is 16 days.
There are currently many Earth observation satellites having different spatial
resolutions, temporal resolutions, radiometric resolutions and spectral resolutions. Table
2 compares these resolutions of some well-known satellites.
Table 2. Some featured satellite images
Satellite
image
Type
Typical
spatial
resolution
Spectral
resolution
(exclude
panchromatic)
Radiometric
resolution
Temporal
resolution
1
MODIS
Optical
250
–
1000m
36 bands
12 bits
Daily
2
SPOT 5
Optical
10m
4 bands (Green,
Red, Near IR,
SWIR)
8 bits
2-3
days,
depending
on latitude
3
Landsat 8
Optical
30m
10 bands (Coastal
-> TIRS2)
12 bits
16 days
4
Sentinel 2A
Optical
10 – 20m
12 bands (Coastal
-> SWIR)
12 bits
10 days
17
2.2.2. Landsat 8 images
The 8th Landsat satellite – Landsat 8 (Figure 7) was successfully launched into orbit
on February 12, 2013. This is a joint project between NASA and the US Geological
Survey. Landsat 8 satellite provides medium resolution images (from 15 to 100 meters),
with polar coverage.
Figure 7. Landsat 8 images4
Landsat 8 satellite has two sensors: Operational Land Imager (OLI) and Thermal
InfraRed Sensor (TIRS). These two sensors provide images at a spatial resolution of 30
meters for visible/near infrared/infrared bands, 100 meters for thermal bands and 15
meters for panchromatic band. For the thermal bands, the manufacturer increased their
spatial resolution up to 30m through a resampling procedure. The ground coverage of a
Landsat 8 image is limited to 185km x 180km. Satellite altitude reaches 705 km.
A comparison of Landsat 7 and Landsat 8 bands is provided in Figure 8:
4 NASA’s Goddard Space Flight Center
18
Figure 8. Landsat 7 and Landsat 8 bands5
Landsat 8 is programmed to fly around the Earth for 99 minutes, covers the entire
surface of the Earth for 16 days. With about 400 images acquired per day, Landsat 8
satellite provides a more accurate view of Earth’s variations within 10 years of its life.
Landsat 8 images are provided to users via the Internet. Each image product is a
compressed file containing 12 TIFF image files and a metadata file. Landsat 8 images
are stored in raster format, which means that they are made up of pixels. Each image is
a grid of pixels. Among the 12 TIFF files, 11 files are numbered from 1 to 11 indicating
the band number. Each of the files stores energy values that the sensors receive in 16-
bit integer format which is also known as digital numbers (DN) (Table 3). The remaining
file is a BQA file added by the manufacturer.
Table 3. Landsat 8 bands6.
Band
Name
Central wavelength
(µm)
Spectral range (µm)
1
Coastal Aerosol (OLI)
0.443
0.433-0.453
2
Blue (OLI)
0.482
0.450-0.515
3
Green (OLI)
0.562
0.525-0.600
4
Red (OLI)
0.655
0.630-0.680
5 Website http://www.imagico.de/map/landsat8.php
6 Website http://landsat.gsfc.nasa.gov/?page_id=5377
19
5
NIR (OLI)
0.865
0.845-0.885
6
SWIR 1 (OLI)
1.610
1.560-1.660
7
SWIR 2 (OLI)
2.200
2.100-2.300
8
Panchromatic (OLI)
0.590
0.500-0.680
9
Cirrus (OLI)
1.375
1.360-1.390
10
Thermal 1
10.8
10.3-11.3
11
Thermal 2
12.0
11.5-12.5
In this study, I used Landsat 8 Surface Reflectance images (Figure 8). Landsat 8
Surface Reflectance data are generated from the Landsat Surface Reflectance Code
(LaSRC), which makes use of the coastal aerosol band to perform aerosol inversion tests
[16]. LaSRC has a unique radiative transfer model and it also uses auxiliary climate data
from MODIS sensor. Figure 9 shows a Landsat 8 image before and after atmospheric
correction. In the uncorrected image (left), it could be clearly seen impacts of
atmosphere in blurred areas (exclude cloudy areas). This impact is significantly reduced
in the corrected image (right).
Currently, Landsat 8 SR data product contains seven bands including Coastal
Aerosol, Blue, Green, Red, NIR, SWIR1, SWIR2. Besides, there are also cloud mask
bands, and some ancillary data.
Figure 9. Comparison of Landsat 8 OLI (left) and SR (right) images.
20
2.3.
Compositing methods
Optical satellite images have a big drawback. In particular, they are heavily impacted
by clouds. If a region is covered by clouds during its satellite passing time, the recorded
data is considered lost. Therefore, methods for tackling clouds in optical satellite images
have been studied by many researchers. Pixel-based image compositing is a paradigm
in remote sensing science that focuses on creating cloud-free, radiometrically and
phenologically consistent image composites. The image composites are spatially
contiguous over large areas [17]. In the past, some compositing methods for low spatial
resolution images (i.e. 500x500m or greater) were developed [18], [19]. Those methods
were used primarily to reduce the impacts of clouds, aerosol contamination, data volume
and view angle effects which are inherent in the images. Due to high temporal resolution
of the satellites, the compositing methods were relatively simple, i.e. use maximum
Normalized Difference Vegetation Index (NDVI) or minimum view angle to pick an
appropriate observation for a target pixel. Since the opening of the Landsat archive,
compositing methods for Landsat images have been developed and benefitted by pre-
existing approaches for MODIS and AVHRR data.
Recently, a number of best-available-pixel compositing (BAP) methods have been
proposed for medium/high satellite images. Generally, BAP methods replace cloudy
pixels with best-quality pixels from a set of candidates through rule-based procedures.
Selection rules are based on spectral-related information, that is, maximum normalized
difference vegetation index (NDVI) [20] and median near-infrared (NIR) [21]. On
another approach, Griffiths et al. proposed a BAP method ranking candidate pixels by
score set such as distance to cloud/cloud shadow, year, and day-of-year (DOY) [22].
This method was improved by incorporating new scores for atmospheric opacity and
sensor types [17]. Gómez et al. recently offered a review emphasizing BAP potential
for monitoring in cloud-persistent areas [23], which includes applications in forest
biomass, recovery and species mapping [24], [25], [26], change detection applications
[27], and general land-cover applications [28].
A summary of several compositing methods is presented in Table 4.
Table 4. Review of compositing methods for satellite images.
Study
Satellite
images
Method
1 Hanse
n et al.
2008
[29]
Landsat
5, 7
ܲ݅ݔ݈݁௦௧ௗൌmin൛ܲ
௨ௗ&௦ௗ௪
ଵ௦௧ೌೌ, … , ܲ
௨ௗ&௦ௗ௪
௧ೌೌൟ
Where:
ܲ݅ݔ݈݁௦௧ௗ: the candidate pixel selected for composition
21
ܲ
௨ௗ&௦ௗ௪
௧ೌೌ: probability of cloud/cloud shadow of the same
pixel in nth candidate image
If two or more candidate pixels have equal Pcloud&shadow, then
choose the pixel value closest to a forest reference value (100)
2 Roy et
al.
2010
[20]
Landsat
5, 7
ܲ݅ݔ݈݁௦௧ௗ
ൌቊmaxሼܰܦܸܫଵ௦௧ೌೌ, … , ܰܦܸܫ௧ೌೌሽ, ݂݅ ݒ݁݃݁ݐܽݐ݁݀ ݅ݔ݈݁ݏ
maxሼܤܶܧܯଵ௦௧ೌೌ, … , ܤܶܧܯ௧ೌೌሽ, ݂݅ ݑ݊ݒ݁݃݁ݐܽݐ݁݀ ݅ݔ݈݁ݏ
Where:
NDVI: Normalized Difference Vegetation Index
BTEM: Brightness Temperature
Eligible candidate pixels must be of minimal cloud, snow, and
atmospheric contamination
3 Potap
ov et
al.
2011
[21]
Landsat 7 ܲ݅ݔ݈݁௦௧ௗൌmin absሼܰܫܴଵ௦௧ೌೌെܰܫܴௗ , … , ܰܫܴ௧ೌೌ
െܰܫܴௗ ሽ
Where:
NIR: near infrared spectral band
Only satellite images acquired in growing seasons are eligible
for ranking procedure
4 White
et al.
2014
[17]
Landsat
5, 7
ܲ݅ݔ݈݁௦௧ௗ
ൌmaxሼݏݑ݂݉ݏܿݎ݁ݏሺܽܿ݅ݐݕ, ݈ܿݑ݀&ݏ݄ܽ݀ݓ, ݀ݕ, ݏ݁݊ݏݎሻଵ௦௧ೌೌ, . . ሽ
2.4.
Machine learning methods in land cover study
Basically, LC classification is a type of classification on image data. Therefore,
machine learning classifiers are also applicable to LC classification. In fact, there existed
a huge amount of researches on machine learning classifiers in LCC. These methods
range from simple thresholding to more advanced approaches such as maximum
likelihood, logistic regression, decision tree (ID3, C4.5, C5), random forest, support
vector machine (SVM), artificial neuron network (ANN) and so on [30], [31], [32],
[33], [34]. Some well-known classifiers are presented below.
2.4.1. Logistic Regression
Logistic regression is a generalized linear model which is often used for
classification. Suppose the training data represented by {xi, yi}, i = 1, … , k, where x ∈
