=Paper=
{{Paper
|id=Vol-2210/paper50
|storemode=property
|title=Land cover classification and build spectral library from hyperspectral and multi-spectral satellite data: A data comparison study in Samara, Russia
|pdfUrl=https://ceur-ws.org/Vol-2210/paper50.pdf
|volume=Vol-2210
|authors=Mukesh Boori,Rustam Paringer,Komal Choudhary,Alexander Kupriyanov,Rukmini Banda
}}
==Land cover classification and build spectral library from hyperspectral and multi-spectral satellite data: A data comparison study in Samara, Russia==
https://ceur-ws.org/Vol-2210/paper50.pdf
Land cover classification and build spectral library from
hyperspectral and multi-spectral satellite data: A data
comparison study in Samara, Russia
M S Boori1, 2, R Paringer1, 3, K Choudhary1, A Kupriyanov1, 3 and R Banda4
1
Samara National Research University, Moskovskoye Shosse 34, Samara, Russia, 443086
2
American Sentinel University, Suite 310, Aurora, Colorado, USA
3
Image Processing Systems Institute - Branch of the Federal Scientific Research Centre
“Crystallography and Photonics” of Russian Academy of Sciences, Molodogvardeyskaya str.
151, Samara, Russia, 443001
4
Research Centre imarat (RCI), Defence Research & Development Organisation (DRDO)
Hyderabad, India
Abstract The purpose of this research work is to compare hyperspectral and multispectral
imagery to discriminating land-cover classes by k-nearest neighbor algorithm (KNN)
supervised classification with migrating means clustering unsupervised classification (MMC)
method and in last develop spectral library. We used Earth Observing-1 (EO-1) Hyperion
hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager
(ALI) multispectral data. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89,
.85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised
classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI
respectively). In addition, it is demonstrated that the hyperspectral satellite image provides
more accurate classification results than those extracted from the multispectral satellite image.
The higher classification accuracy by KNN supervised was attributed principally to the ability
of this classifier to identify optimal separating classes with low generalization error, thus
producing the best possible classes’ separation.
1. Introduction
Remote sensing data are commonly used for land cover classification and mapping and its replaced
traditional classification methods, which is expensive and time consuming. Since the early 1970s,
multispectral satellite data have been widely used for land cove classification [1]. Multispectral remote
sensing technologies, in a single observation, collect data from three to six spectral bands from the
visible and near-infrared region of the electromagnetic spectrum [2]. This crude spectral categorization
of the reflected and emitted energy from the earth is the primary limiting factor of multispectral
sensors either spatially or spectrally to monitor sub-class level classification as they have very similar
characteristics. Increasing the number of ‘‘pure pixels’’ through improved spatial resolution removes a
large source of error in the remote sensing analysis classification. Species level mapping works well
for monotypic stands, which occur in large stratifications [3]. Where species are more randomly
distributed or patchy at fine scales (grain), accurate map classifications are difficult to obtain. So over
the past two decades, the development of airborne and satellite hyperspectral sensor technologies has
overcome the limitations of multispectral sensors [4]. Hyperspectral sensors collect several, narrow
IV International Conference on "Information Technology and Nanotechnology" (ITNT-2018)
Image Processing and Earth Remote Sensing
M S Boori, R Paringer, K Choudhary, A Kupriyanov and R Banda
spectral bands from the visible, near-infrared, mid-infrared and short-wave infrared portions of the
electromagnetic spectrum [5]. These sensors typically collect more than 200 spectral bands, enabling
the construction of an almost continuous spectral reflectance signature [6]. These bands are so
sensitive to ground features that it is possible to record detailed information about earth surface. In
addition, materials which have similar spectral features are possible to be discriminated [7]. However,
to date, there is little research working on hyperspectral satellite data for land cover and land use
mapping. As a result, accurate classification results with various land cover and land use classes are
expected to be derived from a hyperspectral satellite image. Furthermore, narrow bandwidths
characteristic of hyperspectral data permit an in-depth examination of earth surface features which
would otherwise be ‘lost’ within the relatively coarse bandwidths acquired with multispectral data
classification [8].
There are two broadways of classification procedures: (1) unsupervised classification and (2)
supervised classification. Unsupervised classification algorithms require the analyst to assign labels
and combine classes after the fact into useful information classes (e.g. forest, agricultural, water, etc).
In many cases, this after the fact assignment of spectral clusters is difficult or not possible because
these clusters contain assemblages of mixed land cover types. Generally speaking, unsupervised
classification is useful for quickly assigning labels to uncomplicated, broad land cover classes such as
water, vegetation/non-vegetation, forested/non-forested, etc). Furthermore, unsupervised classification
may reduce analyst bias. Supervised classification allows the analyst to fine tune the information
classes--often too much finer subcategories, such as species level classes. Training data is collected in
the field with high accuracy GPS devices or expertly selected on the computer [9]. Consider for
example if you wished to classify percent crop damage in corn fields. A supervised approach would be
highly suited to this type of problem because you could directly measure the percent damage in the
field and use these data to train the classification algorithm. Using training data on the result of an
unsupervised classification would likely yield more error because the spectral classes would contain
more mixed pixels than the supervised approach. Similarly, collecting in the field crop species training
data is preferable to expertly selecting pixels on screen as it is often very difficult to determine which
crops are growing visually [10]. Many studies have reviewed the application of hyperspectral and
multispectral imagery in the classification and mapping of land use in particular water, urban,
transportation and vegetation species level by detecting biochemical and structural differences. The
main aim of this study is to evaluate k-nearest neighbor algorithm (KNN) supervised classification
with migrating means clustering unsupervised classification (MMC) method on hyperspectral and
multispectral imagery to discriminating land-cover classes [11]. For this purpose, a test site was
selected an area located in the mainland of Samara region, Russia for which hyperspectral and
multispectral imagery were made available. This research work focuses on the classification of
multispectral and hyperspectral satellite imagery, in order to: (1) test the potential of hyperspectral
satellite data for land cover classification till sub class levels; (2) evaluate the mapping performance of
multispectral and hyperspectral satellite images and (3) finally develop spectral library.
2. Study site
We choose Samara region as a study area and its geographic coordinates are 53°12´10´´N,
50°08´27´´E (fig. 1).
Figure 1. Study area image, Samara region, Russia (source: Google Earth).
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Image Processing and Earth Remote Sensing
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3. Methods
3.1. Selection of satellite data
In this research work we consider spatial, spectral and temporal resolution as well as cost and
availability of data, when we reviewing most appropriate data. The Hyperion hyperspectral sensor
(United States Geological Survey Earth Resources Observation Systems) and the multispectral OLI
and ALI sensor [6] were then selected for this study. Few characteristics of all three sensors are
showing in table 1.
Table 1. Characteristics of Hyperion, OLI and ALI sensors.
No. Characteristics Values
Hyperion OLI ALI
1 Sensor type Push-broom Push-broom Push-broom
2 Wavelength range 400-2.500 nm 434-1.383 nm 433-2.350
3 Number of spectral bands 242 9 7
4 Spectral resolution 10 nm 15 – 200 nm 5 – 30 nm
5 Spatial resolution 30 m 30 m 30 m
6 Swath 7.5 km 185 km 37 km
7 Digitization 12 bits 12 bits 12 bits
8 Altitude 705 km 705 km 705 km
9 Repeat 16 day 16 day 16 day
3.2. Field work and ground trothing
Table 2. Land cover classes and their sub-classes in study area.
No. Class level I Class level II Class level III
1. Water 1.1 Inland water body 1.1.1 Deep water
1.1.2 Shallow water
1.1.3 Turbid water
1.1.4 Clean water
1.2 Lake
1.3 River
2. Vegetation 2.1 Forest 2.1.1 Conifer forest
2.1.2 Deciduous/Broadleaved forest
2.1.3 Mixed forest
2.2 Agriculture 2.2.1 Heterogeneous agricultural area
2.2.2 Permanent crops
2.3 Mangroves
2.4 Grassland
2.5 Sparsely vegetated area
3. Settlements 3.1 residential 3.1.1 Old residential
3.1.2 New residential
3.2 Industrial
3.3 Park
4. Wetland
5. Bare land 5.1 Scrubland
5.2 Transitional woodland
6. Transportation 6.1 Road 6.1.1 Highway
6.1.2 Inside road
6.1.3 Concrete road
6.2 Rail
7. Bare rocks
8. Sand dunes
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Fieldwork to map individual land cover classes and obtained spectral measurements of the
dominant species was conducted at 60 sites in Samara region, Russia. Ground-trothing surveys should
be undertaken within two weeks of acquiring satellite remote sensing imagery [7]. A random sampling
method was used across the Samara region, around 7-8 samples selected in each class. The FieldSpec
3 ASD handheld spectrometer was used to obtain quantitative measurements of radiant energy easily
and efficiently. We find eight meagre land cove classes and their sub-classes as shown in table 2.
3.3. Data preprocessing
OLI ALI Hyperion
Figure 2. A sub-scene of the geometrically corrected OLI, ALI and Hyperion image over the study
area in Samara region, Russia.
Digital image processing was manipulated in ArcGIS software. The scenes were selected to be
geometrically corrected, calibrated and removed from their dropouts. All images were projected in
UTM 39N, datum WGS 84 projection. Other image enhancement techniques like histogram
equalization were also performed on each image for improving the quality of the image [8]. Some
additional supporting data were also used in this study such as topographic sheets and field data.
Digital topographical maps, 1:50,000 scale, were used for image georeferencing for the land use/cover
map and for improving accuracy of the overall assessment. Using ArcMap, we made a composite
raster data of OLI and ALI using Arc toolbox data management tools (fig. 2). Both images were
composed of 9 and 7 different bands respectively, each representing a different portion of the
electromagnetic spectrum. By combining all these bands, composite raster data were obtained (fig. 2).
Table 3 shows details of OLI and ALI data. For pre-processing of Hyperion imagery, first
georeferenced the image, subsequently were removed the non-calibrated bands of the Hyperion
imagery. After this step, the resulting image was reduced to a subset of the studied region. These final
132 bands after this last pre-processing step were used in the present study (fig. 2).
Table 3. Left: Wavelength ranges of the OLI image. Right: Wavelength ranges of the ALI image.
OLI Bands Wavelength Resolution ALI Wavelength Resolution
(micrometers) (meters) Bands (micrometers) (meters)
Band 1 - Ultra Blue 0.435 - 0.451 30 Pan 0.48 - 0.69 10
Band 2 - Blue 0.452 - 0.512 30 MS - 1' 0.433 - 0.453 30
Band 3 - Green 0.533 - 0.590 30 MS - 1 0.45 - 0.515 30
Band 4 - Red 0.636 - 0.673 30 MS - 2 0.525 - 0.605 30
MS - 3 0.63 - 0.69 30
Band 5 - Near Infrared (NIR) 0.851 - 0.879 30
MS - 4 0.775 - 0.805 30
Band 6 - Shortwave Infrared 1.566 - 1.651 30 0.845 - 0.89 30
MS - 4'
Band 7 - Shortwave Infrared 2.107 - 2.294 30 MS - 5' 1.2 - 1.3 30
Band 8 - Panchromatic 0.503 - 0.676 15 MS - 5 1.55 - 1.75 30
Band 9 - Cirrus 1.363 - 1.384 30 MS - 7 2.08 - 2.35 30
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3.4. Classification
In this research work we use USGS land use/cover classification system for all three images (fig. 3).
For all three images, k-nearest neighbor algorithm (KNN) supervised classification and migrating
means clustering unsupervised classification (MMC) approach was applied [9]. Training sites were
collected based on field data and also take help with topography maps. Initially, training sites were
chosen for all 27 sub-classes derived from all three images, than all 27 sub-classes were aggregated
into following 8 meagre classes 1. Water; 2. Vegetation; 3. Settlements; 4. Wetland; 5. Bare land; 6.
Transportation; 7. Bare rocks and 8. Sand dunes. For accuracy assessment 60 points were randomly
collected in each image.
Figure 3. Flow diagram of methodological process.
3.4.1. Unsupervised classification
In unsupervised classification, image processing software classifies an image based on natural
groupings of the spectral properties of the pixels, without the user specifying how to classify any
portion of the image. Conceptually, unsupervised classification is similar to cluster analysis where
observations (in this case, pixels) are assigned to the same class because they have similar values. The
user must specify basic information such as which spectral bands to use and how many categories to
use in the classification or the software may generate any number of classes based solely on natural
groupings. Common clustering algorithms include K-means clustering, ISODATA clustering, and
Narenda-Goldberg clustering [12].
Unsupervised classification yields an output image in which a number of classes are identified and
each pixel is assigned to a class. These classes may or may not correspond well to land cover types of
interest, and the user will need to assign meaningful labels to each class. Unsupervised classification
often results in too many land cover classes, particularly for heterogeneous land cover types, and
classes often need to be combined to create a meaningful map. In other cases, the classification may
result in a map that combines multiple land cover classes of interest, and the class must be split into
multiple classes in the final map. Unsupervised classification is useful when there is no preexisting
field data or detailed aerial photographs for the image area and the user cannot accurately specify
training areas of known cover type. Additionally, this method is often used as an initial step prior to
supervise classification (called hybrid classification). Hybrid classification may be used to determine
the spectral class composition of the image before conducting more detailed analyses and to determine
how well the intended land cover classes can be defined from the image [13].
3.4.2. Supervised classification
In supervised classification the user or image analyst “supervises” the pixel classification process. The
user specifies the various pixels values or spectral signatures that should be associated with each class.
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This is done by selecting representative sample sites of known cover type called Training Sites or
Areas. The computer algorithm then uses the spectral signatures from these training areas to classify
the whole image. Ideally the classes should not overlap or should only minimally overlap with other
classes. In ArcGIS software there are many different classification algorithms and we choose KNN
supervised classification procedure as:
K-nearest neighbour algorithm (KNN): K nearest neighbour is a simple algorithm that stores
all available cases and classifies new cases based on a similarity measure (e.g., distance
functions). KNN has been used in statistical estimation and pattern recognition already in the
beginning of 1970's as a non-parametric technique. Pattern recognition is the scientific
discipline whose goal is the classification of objects into a number of categories or classes.
Depending on the application, these objects can be images or signal waveforms or any type of
measurements that need to be classified. We will refer to these objects using the generic term
patterns.
In supervised classification the majority of the effort if done prior to the actual classification. Once
the classification is run the output is a map with classes that are labelled and correspond to information
classes or land cover types. Supervised classification can be much more accurate than unsupervised
classification, but depends heavily on the training sites, the skill of the individual processing the
image, and the spectral distinctness of the classes. If two or more classes are very similar to each other
in terms of their spectral reflectance (e.g., annual-dominated grasslands vs. perennial grasslands)
misclassifications will tend to be high. Supervised classification requires close attention to
development of training data. If the training data is poor or not representative the classification results
will also be poor. Therefore supervised classification generally requires more times and money
compared to unsupervised classification.
3.4.3. Classification accuracy assessment
Accuracy assessment of the thematic maps produced from the implementation of the supervised and
unsupervised classification techniques on Hyperion, ALI and OLI imagery was also performed in
ArcGIS based on the confusion matrix analysis [10]. As a result, the overall (OA), user’s (UA) and
producer’s (PA) accuracies and the Kappa (Kc) statistic were computed. The OA provides a measure
of the overall classification accuracy and is expressed as percentage (%). OA represents the
probability that a randomly selected point is classified correctly on the map. Kc provides a measure of
the difference between the actual agreement between reference data and the classifier used to perform
the classification versus the chance of agreement between the reference data and a random classifier.
PA indicates the probability that the classifier has correctly labelled an image pixel. UA expresses the
probability that a pixel belongs to a given class and the classifier has labelled the pixel correctly into
the same given class. In performing the accuracy assessment herein, a total of 60 sampling points for
the different classes were selected (approximately 25 pixels per class) directly from the imagery
following a random sampling strategy, and these points formed our validation dataset. Selection of
those validation points was performed following exactly the same criteria used for the selection of
training points, described earlier (Section 3.2). For consistency, the same set of validation points were
used in evaluating the accuracy of the land use/cover thematic maps produced.
4. Results and discussion
4.1. Developing the spectral library
The land cover spectral library was developed by collected spectra of different sites from all three data
sates and later on used as a set of reference spectra (fig. 4), to define different classes and mixed
communities in Samara region, Russia. The average spectra illustrate a typical pattern, with significant
divergence in the shape of the spectral curve between different land cover classes. The resulted
spectral library shows all land cover class separation is possible in infrared region for all three data. In
compare of all three datasets, all classes can easily separate in Hyperion data, as it have continues
spectral band with very narrow bandwidth so specific bandwidth is sensitive for specific land cover
class. ALI and OLI data have less capacity to separate all land cover class in compare of Hyperion
IV International Conference on "Information Technology and Nanotechnology" (ITNT-2018) 395
Image Processing and Earth Remote Sensing
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data due to less number of bands and longer bandwidth (fig. 4). In compare of ALI and OLI data sets,
ALI has better results due to specific quality of sensor.
a)
b)
c)
Figure 4. Representative spectra for 27 land cover classes by (A) OLI, (B) ALI and (C) Hyperion data
in Samara, Russia.
Samara region land cover classes were defined into 8 major and 27 sub-classes based on species
abundances and the characteristic dominant and sub-dominate land covers. For purposes of building
the spectral library, a good understanding of the all land cover classes at each location in the study
area was needed to utilize fully the information content of the spectra. Intra-specific and
intracommunity variation were found across disturbance gradients. Phenomena included pattern,
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shape-size, water content, structural changes, reduced biomass, lower ‘‘greenness’’ and chlorophyll,
chlorosis and corresponding shifts across the spectral response curve. Methodological approaches to
account for this variability, which can be used to assess stress, are still to be resolved. Large sets of
reference spectra may be needed to fully characterize this variability. However, in this study, some
land cover classes have similar spectral signature in different locations give additional benefits to sub-
class level or species level mapping without a priori knowledge. However similar reflectance of mixed
classes create confusing and difficult to identify class without field data or additional testing of
spectral un-mixing and other spectral matching techniques.
4.2. Using spectral library for land cover classification
OLI Supervised Land Cover ALI Supervised Land Cover Hyperion Supervised Land Cover
OLI Unsupervised Land Cover ALI Unsupervised Land Cover Hyperion Unsupervised Land Cover
Land Cover Classes
1.1.1 Deep water 4 Wetland 2.3 Mangroves 3.1.2 New residential
1.3 River 7 Bare rocks 5.2 Transitional woodland 3.2 Industrial
1.1.4 Clean water 6.1.1 Highway 2.1.3 Mixed forest 3.1.1 Old residential
1.2 Lake 6.2 Rail 8 Sand dunes 3.3 Park
1.1.2 Shallow water 2.1.2 Deciduous forest 2.1.1 Conifer forest 2.5 Sparsely vegetated area
1.1.3 Turbid water 5.1 Scrubland 6.1.2 Inside road 2.4 Grassland
6.1.3 Concrete road 2.2.1 Heterogeneous agricultural area 2.2.2 Permanent crops
Figure 5. OLI, ALI and Hyperion images classified land cover maps by supervised and unsupervised
classification methods.
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Simple land use/cover classes such as forest, agriculture, settlements, water body and bare land can
easily classify in high resolution data, even for their classification, we no need to use spectral library.
Figure 5 show lulc images for all three data sets and in these images major land cover classes such as
vegetation, water etc. can easily identify. As distinct land cover class patterns are closely related with
specific bands/channels so without field data or spectral library or site situation/condition, these
patterns cannot be identify, so basically, we need spectral library for sub-class level land cover
classification.
A land cover map based on spectral library on hyperspectral (Hyperion) and multispectral data
(OLI, ALI) produce 27 land cover classes (fig. 5). In comparison, hyperspectral data provide better
results in place of multispectral data. This finding is similar to [8], who found that spectral resolution
was more important for correct classification than spatial resolution, except in cases where high within
pixel heterogeneity exceeded the pixel-to-pixel variance. In this research work a similar classification
was produced from reference spectra extracted from the image (using GPS coordinates to identify
classes) as from field-measured spectra of those land cover classes and resulted land cover map is a
good representation of spectral pattern change due to continuous spectral bands in hyperspectral data.
Now we can say for wider use of hyperspectral data require improved methodologies and tools that
facilitate and automate basic analyses and mapping, that can be specifically applied to land cover
requirements. Both field and image methods for obtaining reference library spectra required complex
processing and analysis. If a standard spectral library for land cover classes/ communities can be
developed, it will aid resource managers by allowing them to utilize newer more powerful image
analysis techniques while avoiding the data processing and expertise required to create the database.
[4] similarly concluded that key challenges in applying these technologies on a wider scale included:
building human capacity in advanced science and technology-based approaches, development of low
cost and rugged IR spectroscopy instrumentation and development of decision support systems to help
interpret spectroscopy data.
4.3. Classification comparison
The LULC maps produced by supervised and unsupervised classification on Hyperion, ALI and OLI
data acquired over the study region are demonstrated in figure 5. The statistical results of classification
accuracy assessment are shown in table 4. On the basis of accuracy assessment results, its appear that
supervised classification somehow better results than unsupervised classification in overall accuracy
and individual classes accuracy. Results indicate that for KNN the overall accuracy was 95, 94, 88 and
kappa coefficient .91, .89, .85 for Hyp, ALI, OLI respectively, whereas for unsupervised it was 93, 90,
84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively. Among the two
classifiers, supervised classification was the best in describing the spatial distribution and the cover
density of each land cover category, as was also indicated from the statistics of the individual classes’
results produced (table 4).
In all classes similar patterns were easily identify in both classification. PA and UA for the
supervised classification ranged between the classes from 86% to 99%, and from 79% to 94%,
whereas for unsupervised classification varied from 82% to 95% and from 75% to 92% respectively.
In both classification the highest accuracy were in turbid water, permanent crops, sparsely vegetated
area and bare rocks classes, followed by deep water, industrial, mixed forest, grassland, highway and
sand dunes classes. In individual classes the lowest PA and UA in both classifications were shallow
water, clean water, turbid water, grassland and highway classes. For all three data the highest PA and
UA present in Hyperion data and lowest value present in OLI data. This was perhaps due to the
similar spectral characteristics between the two classes, which was affected by the mixed pixels,
caused by the low density of these vegetation types and combined with the low spatial resolution of
the sensors.
So overall we can say supervised classification is better than unsupervised classification. In
unsupervised classification algorithms require the analyst to assign labels and combine classes after
the fact into useful information classes (e.g. forest, agricultural, water, etc). In many cases, this after
the fact assignment of spectral clusters is difficult or not possible because these clusters contain
assemblages of mixed land cover types. Generally speaking, unsupervised classification is useful for
IV International Conference on "Information Technology and Nanotechnology" (ITNT-2018) 398
Image Processing and Earth Remote Sensing
M S Boori, R Paringer, K Choudhary, A Kupriyanov and R Banda
quickly assigning labels to uncomplicated, broad land cover classes such as water, vegetation/non-
vegetation, forested/non-forested, etc). Furthermore, unsupervised classification may reduce analyst
bias. But supervised classification allows the analyst to fine tune the information classes--often too
much finer subcategories, such as species level classes. Training data is collected in the field with high
accuracy GPS devices or expertly selected on the computer. Consider for example if you wished to
classify percent crop damage in corn fields. A supervised approach would be highly suited to this type
of problem because you could directly measure the percent damage in the field and use these data to
train the classification algorithm. Using training data on the result of an unsupervised classification
would likely yield more error because the spectral classes would contain more mixed pixels than the
supervised approach. Similarly, collecting in the field crop species training data is preferable to
expertly selecting pixels on screen as it is often very difficult to determine which crops are growing
visually. That`s why supervised classification is outperformed the unsupervised classification. When
we compare both classification in hyperspectral and multispectral data, results show that supervised
classification have highest accuracy, which authors attributed to the supervised ability to locate an
optimal separating hyperplane [11].
Table 4. Summary of the results from the classification accuracy assessment conducted.
Supervised Classification Unsupervised Classification
Producer’s User’s accuracy Producer’s User’s accuracy
Land cover classes
accuracy (%) (%) accuracy (%) (%)
Hyp ALI OLI Hyp ALI OLI Hyp ALI OLI Hyp ALI OLI
1.1.1 Deep water 98 91 88 90 83 84 95 86 85 88 80 81
1.1.2 Shallow water 94 93 86 87 86 78 92 90 82 85 81 75
1.1.3 Turbid water 99 93 87 91 86 79 94 90 84 90 82 76
1.1.4 Clean water 95 92 87 87 86 78 91 87 83 86 83 75
1.2 Lake 95 93 87 87 85 82 90 91 82 84 81 80
1.3 River 91 93 88 85 88 80 88 90 85 81 85 79
2.1.1 Conifer forest 94 93 88 89 86 82 89 89 86 84 82 80
2.1.2 Deciduous/ Broadleaf
92 99 92 83 92 86 90 96 90 80 90 81
forest
2.1.3 Mixed forest 92 97 92 84 91 86 91 94 90 81 89 82
2.2.1 Heterogeneous
94 92 90 87 86 81 90 87 89 83 82 80
agricultural area
2.2.2 Permanent crops 99 92 90 94 88 85 95 88 89 92 85 81
2.3 Mangroves 96 93 91 91 88 87 92 90 90 90 83 85
2.4 Grassland 95 97 88 89 91 79 91 94 85 86 90 76
2.5 Sparsely vegetated area 99 92 88 91 84 82 96 88 84 90 81 81
3.1.1 Old residential 95 94 86 90 88 81 91 90 82 89 83 80
3.1.2 New residential 94 94 87 85 85 80 90 90 84 82 80 77
3.2 Industrial 98 94 89 93 88 85 95 91 86 91 84 81
3.3 Park 93 93 87 88 85 81 90 90 85 86 81 78
4. Wetland 94 93 88 86 88 80 91 90 84 84 86 79
5.1 Scrubland 96 92 88 89 88 81 91 89 84 85 85 78
5.2 Transitional woodland 95 92 95 87 85 85 90 90 92 83 80 82
6.1.1 Highway 94 97 87 89 91 79 89 94 84 86 90 76
6.1.2 Inside road 92 99 87 86 94 81 88 95 83 82 91 80
6.1.3 Concrete road 93 92 86 85 86 81 87 89 82 81 82 77
6.2 Rail 96 96 87 86 86 81 90 91 82 81 81 79
7. Bare rocks 99 94 88 94 86 83 94 90 85 91 83 81
8. Sand dunes 95 97 88 89 88 84 91 92 86 86 86 82
Overall accuracy 95 94 88 93 90 84
Kappa coefficient .91 .89 .85 .89 .87 .81
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5. Conclusions
This research work demonstrates the potential of hyperspectral and multispectral data for land cover
monitoring and assessment. Currently, limitations of both data availability and cost remain, as do
significant methodological and technical issues. However this research work highlights developing
spectral library for land cover classes. In order to facilitate a global approach to applications of new
advanced technologies for mapping and monitoring of landscape, a standardized classification system
for land cover classes should be adopted to make best use of the spectral libraries and to facilitate a
global remote sensing-based monitoring and assessment capacity. Additionally spectral library provide
useful reference framework for landscape assessment and also support and promote new technology in
terms of new space based high resolution hyperspectral instruments for earth observation. The
accuracy assessment results show that supervised classification is better than unsupervised
classification for all three (Hyperion, ALI and OLI) imagery. The higher classification accuracy
reported by supervised classification is mainly attributed to the fact that this classifier has been
designed as to be able to identify an optimal separating hyperplane for classes’ separation, which the
unsupervised may not be able to locate. This research found that, data analysis of hyperspectral
imagery has the potential for improving classification accuracies of land cover and land use over
multispectral imagery with the same resolution. If images were acquired the same day and time, then
accuracies would be even more comparable. The latter, from an operational perspective, can be of
particular importance particularly in the Mediterranean basin, since it can be associated to the mapping
and monitoring of land degradation and desertification phenomena which are frequently pronounced in
such areas.
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