Currency Recognition on Mobile Phones Proposed system modules
Segmentation Feature Extraction Instance Retrieval 1. Building a
Visual Vocabulary 2. Image Indexing Using Text Retrieval Methods 3.
Retrieval Stage 4. Spatial re-ranking 5. Classification Adaptation
to Mobile Performance analysisModule descriptionA. SegmentationThe
images might be captured in a wide variety of environments, in
terms of lighting condition and background while the bill in the
image itself could be deformed. Image segmentation is important not
just for reducing the data to process but also for reducing
irrelevant features (background region) that would affect the
decision-making. This work starts with a fixed rectangular region
of interest (ROI) which is forty pixels smaller from all four sides
than the image itself. This work assumes that a major part of the
bill will be present inside this region. Everything outside this
ROI is a probable background. Once this region is obtained, it must
be extended to a segmentation of the entire object. Let x be an
image and let y be a partition of the image into foreground
(object) and background components. Let xi R3 be the color of the
ith pixel and let yi be equal to +1 if the pixel belongs to the
object and to -1, otherwise. For segmentation this work use a graph
cut based energy minimization formulation. The cost function is
given by
The edge system E determines the pixel neighborhoods and is the
popular eight-way connection. The pair wise potential S(yi , yj|x)
favors neighbor pixels with similar color to have the same label.
Then the segmentation is defined as the minimize arg miny E(x,y).
We use the Grab Cut algorithm, which is based on iterative graph
cuts, to carry out foreground/ background segmentation of the
images captured by the user. The system should be able to segment
the foreground object correctly and quickly without any user
interaction. Whenever the foreground area is smaller than a
pre-decided threshold, a fixed central region of the image is
marked as foreground.B. Instance Retrieval5.3.1. Building a Visual
VocabularyThis work first locates keypoints in the foreground
region of the image (obtained from segmentation) and describes the
key point regions, using any descriptor extractor like SIFT, SURF
or ORB-FREAK . This work obtains a set of clusters of features
using hierarchical K-means algorithm. The distance function between
two descriptors x1 and x2 is given by
Where is the covariance matrix of descriptors. As is standard,
the descriptor space is affine transformed by the square root of so
that Euclidean distance may be used. The set of clusters forms the
visual vocabulary of image. 5.3.2. Image Indexing Using Text
Retrieval MethodsFor every training image, after matching each
descriptor to its nearest cluster, we get a vector of frequencies
(histogram) of visual words in the image. Instead of directly using
visual word frequencies for indexing, we employ a standard term
frequency - inverse document frequency (tf-idf ) weighting. Suppose
there is a vocabulary of k words, then each image is represented by
a k-vector , of weighted word frequencies with components
Here nid is the number of occurrences of word i in document d,
nd is the total number of words in the document d, ni is the total
number of occurrences of term i in the whole database and N is the
total number of documents in the whole database. The weighting is a
product of two terms: the word frequency, and the inverse document
frequency log .However, retrieval on this representation is slow
and requires lots of memory. This makes it impractical for
applications on mobile phones. Therefore, we use an inverted index
for instance retrieval. The inverted index contains a posting list,
where each posting contains the occurrences information (e.g.
frequencies, and positions) for documents that contain the term. To
rank the documents in response to a query, the posting lists for
the terms of the query must be traversed, which can be costly,
especially for long posting lists.5.3.3. Retrieval Stage At the
retrieval stage, this work obtains a histogram of visual words
(query vector) for the test image. Image retrieval is performed by
computing the normalized scalar product (cosine of the angle)
between the query vector and all tf-idf weighted histograms in the
database. They are then ranked according to decreasing scalar
product. This work selects the first 10 images for further
processing.5.3.4. Spatial re-rankingThe Bag of Words (BoW) model
fails to incorporate the spatial information into the ranking of
retrieved images. In order to confirm image similarity, this work
checks whether the key points in the test image are in spatial
consistency with the retrieved images. This work use the popular
method of geometric verification (GV) by fitting fundamental matrix
to find out the number of key points of the test image that are
spatially consistent with those of the retrieved images.5.3.5.
ClassificationIn the voting mechanism, each retrieved image adds
votes to its image class (type of bill) by the number of spatially
consistent key points it has (computed in the previous step). The
class with the highest vote is declared as the result.C. Adaptation
to MobileThe recognition model needed for retrieval cannot be used
directly on a mobile phone because of the memory requirement. The
system was able to adapt the above solution to a mobile environment
by making very significant reductions in complexity, as much as
possible, without sacrificing the effective accuracy. This allows
us to achieve the best possible performance, given the severe
restrictions in various aspects of the pipeline that we have to
contend with.D. Performance analysisIn this step evaluate the
performance metrics such as accuracy, and precision for the
proposed system..
CHAPTER 2INTRODUCTION2.1 Computer ImagingIt can be defined a
acquisition and processing of visual information by computer.
Computer representation of an image requires the equivalent of many
thousands of words of data, so the massive amount of data required
for image is a primary reason for the development of many sub areas
with field of computer imaging, such as image compression and
segmentation. Another important aspect of computer imaging involves
the ultimate receiver of visual information in some case the human
visual system and in some cases the human visual system and in
others the computer itself.Computer imaging can be separate into
two primary categories:1. Computer Vision.2. Image Processing
Fig 1. Computer ImagingHistorically, the field of image
processing grew from electrical engineering as an extension of the
signal processing branch, whereas are the computer science
discipline was largely responsible for developments in computer
vision.2.2 Computer Vision1. Image Analysis: involves the
examination of the image data to facilitate solving vision
problem.The image analysis process involves two other topics:
Feature Extraction: is the process of acquiring higher level image
information, such as shape or color information. Pattern
Classification: is the act of taking this higher level information
and identifying objects within the image.Computer vision systems
are used in many and various types of environments, such as:1.
Manufacturing Systems2. Medical Community3. Law Enforcement4.
Infrared Imaging5. Satellites Orbiting.2.3 Image ProcessingThe
major topics within the field of image processing include:1. Image
restoration.2. Image enhancement.3. Image compression.1.Image
RestorationIs the process of taking an image with some known, or
estimated degradation, and restoring it to its original appearance.
Image restoration isoften used in the field of photography or
publishing where an image was somehow degraded but needs to be
improved before it can be printed
Fig 2. Image restoration2. Image EnhancementInvolves taking an
image and improving it visually, typically by taking advantages of
human Visual Systems responses. One of the simplest enhancement
techniques is to simply stretch the contrast of an
image.Enhancement methods tend to be problem specific. For example,
a method that is used to enhance satellite images may not suitable
for enhancing medical images.Although enhancement and restoration
are similar in aim, to make an image look better. They differ in
how they approach the problem. Restoration method attempt to model
the distortion to the image and reverse the degradation, where
enhancement methods use knowledge of the human visual systems
responses to improve an image visually.
Fig 3. Image Enhancement3.Image CompressionInvolves reducing the
typically massive amount of data needed to represent an image. This
done by eliminating data that are visually unnecessary and by
taking advantage of the redundancy that is inherent in most images.
Image processing systems are used in many and various types of
environments, such as:1. Medical community2. Computer Aided
Design3. Virtual Reality4. Image Processing.
Fig 4. Image Enhancement2.4 Computer Imaging SystemsComputer
imaging systems are comprised of two primary components types,
hardware and software. The hard ware components can be divided into
image acquiring sub system (computer, scanner, and camera) and
display devices (monitor, printer).The software allows us to
manipulate the image and perform any desired processing on the
image data.2.5 DigitizationThe process of transforming a standard
video signal into digital image .This transformation is necessary
because the standard video signal in analog (continuous) form and
the computer requires a digitized or sampled version of that
continuous signal. The analog video signal is turned into a digital
image by sampling the continuous signal at affixed rate. The value
of the voltage at each instant is converted into a number that is
stored, corresponding to the brightness of the image at that point.
Note that the image brightness of the image at that point depends
on both the intrinsic properties of the object and the lighting
conditions in the scene.2.6. Image RepresentationWe have seen that
the human visual system (HVS) receives an input image as a
collection of spatially distributed light energy; this is form is
called an optical image. Optical images are the type we deal with
every day cameras captures them, monitors display them, and we see
them [we know that these optical images are represented as video
information in the form of analog electrical signals and have seen
how these are sampled to generate the digital image I(r , c).The
digital image I (r, c) is represented as a two- dimensional array
of data, where each pixel value corresponds to the brightness of
the image at the point (r, c). in linear algebra terms , a
two-dimensional array like our image model I( r, c ) is referred to
as a matrix , and one row ( or column) is called a vector.The image
types we will consider are:1. Binary ImageBinary images are the
simplest type of images and can take on two values, typically black
and white, or 0 and 1. These types of images are most frequently in
computer vision application where the only information required for
the task is general shapes, or outlines information. For example,
to position a robotics gripper to grasp ) ) an object or in optical
character recognition (OCR). Binary images are often created from
gray-scale images via a threshold value is turned white (1), and
those below it are turned black (0).
Fig 5. Binary Image2. Gray Scale ImageGray _scale images are
referred to as monochrome, or one-color image. They contain
brightness information only brightness information only, no color
information. The number of different brightness level available.
The typical image contains 8 bit/ pixel (data, which allows us to
have (0- 255) different brightness (gray) levels. The 8 bit
representation is typically due to the fact that the byte, which
corresponds to 8-bit of data, is the standard small unit in the
world of digital computer.
Fig 6. Gray Scale Images3. Color ImageColor image can be modeled
as three band monochrome image data, where each band of the data
corresponds to a different color.
Fig 7. Color ImagesThe actual information stored in the digital
image data is brightness information in each spectral band. When
the image is displayed, the corresponding brightness information is
displayed on the screen by picture elements that emit light energy
corresponding to that particular color.Typical color images are
represented as red, green ,and blue or RGB images .using the 8-bit
monochrome standard as a model , the corresponding color image
would have 24 bit/pixel 8 bit for each color bands (red, green and
blue ). 2.7. Introduction to the projectVisual object recognition
on a mobile phone has many applications. In this paper, we focus on
the problem of recognition of currency bills on a low-end mobile
phone. This is an immediate requirement for the visually impaired
individuals. There are around 285 Million people estimated to be
visually impaired worldwide, out of which 39 Million are blind and
246 Million have low vision. The differences in texture or length
of currency bills are not really sufficient for identification by
the visually impaired. Moreover, bills are not as easy to
distinguish by touch as coins. Certain unique engravings are
printed on the bills of different currencies but they tend to wear
away.We adopt an approach based on computer vision on mobile
devices, and develop an application that can run on low end smart
phones. We consider the bills of Indian National Rupee (|) as a
working example, but the method can be extended to a wide variety
of settings. Our problem is challenging due to multiple reasons. We
want all the computations to happen on the phone itself and this
requires appropriate adaptation of the recognition architectures to
a mobile device. Since our application is desired to be usable in a
wide variety of environments (such as in presence of background
clutter, folded bills etc.), we need a robust recognition scheme
that can address these challenges. Also, visually impaired users
may not be able to cooperate with the imaging process by realizing
the environmental parameters (like clutter, pose and
illumination).2.8. Problem Definition Of the ProjectWorking on a
mobile platform brings with it a number of unique challenges that
need to be taken care of. Primarily, the restrictions are in the
memory, the application size, and the processing time. Currently,
the average size of an iOS application is 23MB, while the RAM limit
for a Windows phone application is 150MB. For an application to run
on a mobile phone without affecting the others, it should not use
more than 100MB of storage and 50MB of RAM. Our application
recognizes the bills in two major steps. First we segment the bill
from the clutter. Then we look at the most similar bill in the
database. Though both these problems can be solved with good
performance using many state-of-the-art computer vision algorithms,
they are not really mobile friendly. The recognition model and
other necessary information for our application would typically
require more than 500MB of storage and 200MB of RAM with a direct
implementation.This exceeds practical limits by a large amount. To
be practically useful, the applications response time should not be
more than 4 seconds keeping in mind that the current average
response time is 3.28 seconds.
CHAPTER 3LITERATURE SURVEY1) Monitoring of the Rice Cropping
System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization
Data-Alexandre Bouvet, Thuy Le Toan, and Nguyen Lam-Dao,
2009.Introduction In recent years, changes in cultural practices
have been observed in different regions of the world. The rice
growth region in the Mekong Delta in Vietnam is a good example of
changes from the traditional to modern rice cultivation system in
the last ten years. A multiple cropping system is implemented,
increasing the number of crops per year from one or two to two,
three, or even more. Dike infrastructures have been built and
intensified after 2000 to block the flood way into the fields
during the flood season so as to allow an additional crop cycle.
Short-cycle rice varieties (80100 days) are planted in order to
harvest three crops per year instead of one or two. Finally, modern
water management has been partly introduced in the last three
years, consisting in intermittent drainage between two irrigation
operations. For those changes in cultural practices, the intensity
temporal change method for rice mapping and monitoring needs to be
upgraded. In this work, a method using polarization information is
developed and assessed for this purpose. Because of the vertical
structure of rice plants, the difference between HH and VV
backscattering is expected to be higher than that of other crop or
land cover types, and through the relation with wave attenuation in
the canopy, the ratio of the HH and VV backscattering coefficients
(hereafter called HH/VV) can be related to the vegetation biomass.
A joint analysis of ERS and RADARSAT-1 data , and the modeling of
C-band HH and VV revealed that HH is significantly higher than VV,
and the difference can reach 67 dB at the peak growth stage. Based
on these findings, HH/VV is potentially a good classifier for rice
monitoring, and methods using HH/VV need to be developed and
assessed. Specifically, in this work, the method is developed using
a time series of dual polarization (HH and VV) ASAR data and tested
in the province of An Giang in the Mekong Delta.Advantages This
promising result shows that methods using SAR data can be timely
and cost effective. The method is well-suited to regions where
fields have multiple crops and shifted calendars.Disadvantages Need
to consider the improvement of the method by using HH/VV and the
temporal change of HH and/or VV in the multi date approach.2) Rice
Phenology Estimation With Multitemporal Terrasar-X Data Using
Dynamic System Concepts F. Vicente-Guijalba, T. Martinez-Marin,
J.M. Lopez-Sanchez,IntroductionPrecision farming has been an
important subject during the last decades. The aim of these
agricultural techniques is to optimize the field-level management
regarding to the crop needs, the environmental impact and the
economical competitiveness of the yield. Remote sensing tools based
on SAR have improved coverage and temporal information resolution
for these agricultural practices. Due to its importance in the
human diet, rice has been subject of study in a wide set of remote
sensing works. The first studies with SAR were aimed to detect and
classify rice fields. More recent works have demonstrated that by
means of a set of PolSAR variables it is possible to obtain a
coarse estimation of the phenological stage in rice fields.
Phenology provides a measure of the biological progress within a
crop field and the estimation of this parameter allows farm
managers to plan crop activities in an optimized way. Based on the
previous approaches, where each estimation is obtained for a single
acquisition without using any other information, This work focused
the estimation problem from a dynamic system view. The main
objective is to employ the temporal information provided by the
time series of SAR images to infer the phenological stage in a
particular field and date. The estimation approach consists in two
main stages: the dynamical model generation and the estimation
itself.Advantages The proposed method is able to provide estimation
on rice fields based on dual-pol SAR imagery. It achieves results
with higher resolution ground truth data in order to validate this
methodology.Disadvantages Need to study the generation of models
for other kind of crops that behaves in a similar way and try to
apply an analogous approach to the phonological estimation.3) Rice
Phenology Monitoring by Means of SAR Polarimetry at X-Band- Juan M.
Lopez-Sanchez, Senior Member, IEEE, Shane R. Cloude, Fellow, IEEE,
and J. David Ballester-Berman,2012.Introduction The feasibility of
retrieving the phenological stage of rice fields at a particular
date by employing coherent copular dual-pol X-band radar images
acquired by the TerraSAR-X sensor has been investigated in this
paper. A set of polarimetric observables that can be derived from
this data type has been studied by using a time series of images
gathered during the whole cultivation period of rice. Among the
analyzed parameters, besides backscattering coefficients and
ratios, we have observed clear signatures in the correlation (in
magnitude and phase) between channels in both the linear and Pauli
bases, as well as in parameters provided by target decomposition
techniques, like entropy and alpha from the eigenvector
decomposition. A new model-based decomposition providing estimates
of a random volume component plus a polarized contribution has been
proposed and employed in interpreting the radar response of rice.
By exploiting the signatures of these observables in terms of the
phenology of rice, a simple approach to estimate the phonological
stage from a single pass has been devised. This approach has been
tested with the available data acquired over a site in Spain, where
rice is cultivated, ensuring ground is flooded for the whole
cultivation cycle, and sowing is carried out by randomly spreading
the seeds on the flooded ground.Advantages The proposed method is
simple. It provide better estimation accuracyDisadvantages The main
drawback of using dual-pol TerraSAR-X images for this application
is their narrow swath (around 15 km on the ground), which is too
small for devising a monitoring scheme on large-scale rice
plantations. Noise level of the system (NESZ around 19 dB), which
may result very close or even higher than the backscattering from
rice fields, especially at the early stages of the cultivation
cycle.4) A Kalman Filter Based Mtinsar Methodology For Derving 3d
Surface Displacement Evolutions- Hu J. , Ding X.L. , Li Z.W., Zhu
J.J. , Sun Q., Zhang L., Omura M., 2012.Introduction Multi-temporal
InSAR (MTInSAR) have been used widely for studying earth surface
deformations related to many geophysical processes. However,
MTInSAR techniques have been able to measure one-dimensional (1D)
surface deformations in the direction of the line-of-sight (LOS) of
the radar. As surface deformations are usually three-dimensional,
one-dimensional observation apparently cannot always fully reflect
the actual deformations. In addition, the temporal resolution of
MTInSAR measurements is limited by the satellite orbit repeat
period. The number of SAR satellites has been increasing rapidly in
recent years. It is therefore very desirable to combine the
observations from the different SAR satellites and orbits to derive
more comprehensive surface deformation measurements. This work
present a novel new MTInSAR approach for exploiting multi-sensor,
multi-track and multi-temporal interferograms to infer
three-dimensional (3D) surface displacements. The proposed approach
is based on Kalman filter that has been widely used for modeling
various dynamic processes. First, the 1D LOS measurements are
estimated from multi-sensor, multi-track and multi-temporal
interferograms. The observation model and state models of the
Kalman filter are then constructed by considering the imaging
geometry and temporal correlation. The 3D surface displacement at
all the acquisition times can be estimated based on the models and
a weighting scheme that reflects the noise levels of the
observations and the deformations. The accuracy of the measurements
in the north-south directions is low due to the polar orbits of the
current SAR satellites. In order to ensure the accuracy of the
results in the up and east-west directions, we assume that the
deformation in the north-south direction is negligible in the case
study carried out for the Los Angeles area. The experiment uses 21
SAR acquisitions from ENVISAT ascending and descending orbits and
PALSAR ascending obits. The results are compared with GPS
measurements in the area.Advantages This work can fully utilize the
available interferograms Significantly increase the temporal
monitoring frequency.Disadvantages The 3D instaneous rate vectors
and correspondingly variances are usually difficult to exactly
identify without any priori information.5) Kalman-Filter-Based
Approach for Multisensor, Multitrack, and Multitemporal InSAR - Jun
Hu, Xiao-Li Ding, Zhi-Wei Li, Jian-Jun Zhu, Qian Sun, and Lei
Zhang, Member, IEEE, 2013.Introduction Differential interferometric
synthetic aperture radar (SAR) (InSAR) (DInSAR) has been widely
used for monitoring ground deformation associated with various
geophysical and engineering processes. However, the applications of
DInSAR have been limited by the effects of temporal and spatial
decorrelation, atmospheric artifacts, and the inability of the
method in providing 3-D measurements. Several multitemporal InSAR
(MTInSAR) methods have been developed in recent years to reduce the
effects of temporal and spatial decorrelation and atmospheric
artifacts, including the persistent scatterers, the small-baseline
(SB) subset, and the temporarily coherent point. The measurements
from the MTInSAR approaches are, however, 1-D too, i.e., along the
line of sight (LOS) of the SAR satellite. When the ground moves not
in this direction only, which is, in fact, the case most of the
time, the InSAR measurements cannot fully reflect the actual
deformation. Some efforts have been made to derive 2-D or 3-D
displacement information by combining InSAR measurements from
different orbits or combining InSAR measurements with other types
of measurements such as those from the Global Positioning System
(GPS). This work present a Kalman-filter-based approach for
retrieving 3-D surface displacement from multisensor, multitrack,
and multitemporal SAR interferograms. This approach allows InSAR
measurements from different directions to be integrated
sequentially as they become available so that
high-temporalresolution results can be achieved. The approach is
tested with both simulated and real SAR data sets to verify its
performance.Advantages The method works well when the measurement
noise is low. The proposed approach can be potentially used to
include other measurements, such as GPS and leveling, in the
solutions. It achieves the improved accuracyDisadvantages It
becomes unstable when the measurement noise is high due to the
polar-orbiting imaging geometries of the current satellite SAR
sensors.6) Estimating near future regional corn yields by
integrating multi-source observations into a crop growth model
-Jing Wang, Xin Li Ling Lu, Feng Fang, 2013.IntroductionRegional
crop yield estimations play important roles in the food security of
a society. Crop growth models can simulate the crop growth process
and predict crop yields, but significant uncertainties can be
derived from the input data, model parameters and model structure,
especially when applied at the regional scale. Abundant
observational information provides the relative true value of
surface conditions, and this information includes those areal data
from remote sensors and ground observations. The objective of this
study was to present a data fusion framework used to calibrate a
crop growth model at the plot scale and to estimate yield at the
regional scale on the basis of two types of data fusion algorithms,
which reduces the uncertainty of regional yield estimations. First,
based on local intensive observation, the simulated annealing
algorithm was applied to obtain a parameter vector that was suited
to the local crop variety. This scheme reduces model parameter
uncertainty. Then, the ensemble Kalman filter (EnKF), a sequence
filter algorithm, was adopted to integrate the areal crop growth
information that was derived from remote sensing technologies into
a crop growth model for precise regional yield estimation, which
reduces uncertainties in the model structure or input data related
to meteorological, soil, or filed management information. This
proposed scheme and technology will provide an operational method
for precisely estimating crop yields at regional scales. Advantages
The WOFOST model can simulate the growth curve and yield of corn,
especially with respect to crop carbon absorption in
agri-ecological systems This study aimed to assess the feasibility
of assimilating areal observation data into a crop growth model to
improve spatial estimates of crop yields and carbon
pools.Disadvantages Estimation uncertainty also arises from
parameter uncertainty, and an accurate parameter set is critical
for accurate yield predictions.7) Efficient Spatio-temporal Mining
of Satellite Image Time Series for Agricultural Monitoring- Andreea
Julea, Nicolas Meger, Christophe Rigotti, Emmanuel Trouve, Romain
Jolivet, and Philippe Bolon, 2012.IntroductionThis work presents an
unsupervised technique to support SITS analysis in agricultural
monitoring. The presented approach relies on frequent sequential
pattern extraction along the temporal dimension, combined with a
spatial connectivity criterion. It allows to uncover sets of pixels
satisfying two properties of cultivated areas: they are spatially
connected/grouped and share similar temporal evolutions. The
approach requires no prior knowledge of the objects (identified
regions) to monitor and needs no user-supplied aggregate functions
nor distance definitions. It is based on the extraction of
patterns, called Grouped Frequent Sequential patterns
(GFS-patterns), satisfying a support constraint and a pixel
connectivity constraint. In this paper, we extend the general
framework of GFS-patterns. This work proposed in two directions,
when applied to agricultural monitoring. Firstly, we show that,
even though the connectivity constraint does not belong to any
typical constraint family (e.g., monotonic, anti-monotonic), it can
be pushed partially in the search space exploration. This leads to
significant reduction of execution times on real Satellite Image
Time Series of cultivated areas. Secondly, we show that a simple
post-processing using a maximality constraint over the patterns is
very effective. Indeed, it restricts the number of patterns to a
human-browsable collection, while still retaining highly meaningful
patterns for agro-modelling. This property is confirmed even for
poor quality inputs (rough image quantization, raw noisy
images).Advantages GFS-patterns is used to extract sets of pixels
sharing similar evolution from Satellite Image Time Series over
cultivated areas It achieves reduced GFS-patterns extraction times
Even on poor quality inputs (i.e., noisy images, rough
quantization), the method can exhibit various level of details of
primary interest in agro-modellingDisadvantages The contribution
due to the stratified atmosphere can be roughly estimated by using
DEMs and meteorological data, but the effects of the turbulent
atmosphere still degrade interferograms.8) Integrating Vegetation
Indices Models and Phenological Classification with Composite SAR
and Optical Data for Cereal Yield Estimation in Finland (Part I)-
Heikki Laurila, Mika Karjalainen, Juha Hyypp and Jouko Kleemola,
2010.IntroductionThe aim of the present study was to estimate
actual non-potential grain yield levels for high latitude spring
cereals (spring wheat, barley and oats, Avena Sativa L.) in large
area field conditions in southern Finland. The cereal theoretical
maximum yielding capacity is limited by environmental and
vegetation stresses (e.g., drought periods, nutrient deficiencies,
pathogen epidemics) during growing season in actual field growing
conditions. These stress factors result to reduced non-potential
baseline yield levels (yb, kg/ha) on field parcel level. The
objectives of the present study were: (i) to construct a dynamic
SatPhenClass phonological classification model, which classifies
both optical and SAR satellite data based on cereal actual
phenological development in both vegetative and generative phases
(ii) to calibrate and validate multispectral Composite Vegetation
Indices (VGI) models, which integrate both phenologically
preclassified optical (Models III) and microwave SAR data
(Composite SAR and NDVI Model III), and finally (iii) VGI models
were used to estimate cereal non-potential baseline yield (yb)
levels in growing zones (IIV) in southern Finland during
19962006.Advantages The proposed method is validated to estimate
cereal yield levels using solelyoptical and SAR satellite data. The
averaged composite SAR modeled grain yield level was 3,750 kg/ha
(RMSE = 10.3%, 387 kg/ha) for high latitude spring
cereals.Disadvantages The early emergence in vegetative phase (ap,
BBCH 012) in two leaf stage before double ridge induction and the
senescence phase after full maturity and harvest (dp), BBCH >
90) were difficult to estimate.9) Multi-temporal MODISLandsat data
fusion for relative radiometric normalization, gap filling, and
prediction of Landsat data David P. Roy, Junchang Ju, Philip Lewis
, Crystal Schaaf , Feng Gao, Matt Hansen, Erik Lindquist,
2008.IntroductionA semi-physical fusion approach that uses the
MODIS BRDF/Albedo land surface characterization product and Landsat
ETM+ data to predict ETM+ reflectance on the same, an antecedent,
or subsequent date is presented. The method may be used for ETM+
cloud/cloud shadow and SLC-off gap filling and for relative
radiometric normalization. It is demonstrated over three study
sites, one in Africa and two in the U.S. (Oregon and Idaho) that
were selected to encompass a range of land cover land use types and
temporal variations in solar illumination, land cover, land use,
and phenology. Specifically, the 30 m ETM+ spectral reflectance is
predicted for a desired date as the product of observed ETM+
reflectance and the ratio of the 500 m surface reflectance modeled
using the MODIS BRDF spectral model parameters and the sun-sensor
geometry on the predicted and observed Landsat dates. The
difference between the predicted and observed ETM+ reflectance
(prediction residual) is compared with the difference between the
ETM+ reflectance observed on the two dates (temporal residual) and
with respect to the MODIS BRDF model parameter quality. For all
three scenes, and all but the shortest wavelength band, the mean
prediction residual is smaller than the mean temporal residual, by
up to a factor of three. The accuracy is typically higher at ETM+
pixel locations where the MODIS BRDF model parameters are derived
using the best quality inversions. The method is most accurate for
the ETM+ near-infrared (NIR) band; mean NIR prediction residuals
are 9%, 12% and 14% of the mean NIR scene reflectance of the
African, Oregon and Idaho sites respectively.Advantages The
proposed method Achieves best quality Also achieves higher
accuracyDisadvantages Significant reflectance changes of this
nature are difficult to accommodate using conventional relative
radiometric normalization and gap filling techniques.10) An
automated algorithm to detect timing of urban conversion of
agricultural land with high temporal frequency MODIS NDVI data -
Bhartendu Pandey, Qingling Zhang and Karen C. Seto,
2013.Introduction Urban expansion is one of the major drivers of
agricultural lands loss. However, current remote sensing-based
efforts to monitor this process are limited to small scale case
studies that require much user input. Given the rate and magnitude
of contemporary urbanization, there is a need to develop a land
change algorithm that can characterize the loss of agricultural
land at large scales over long time periods. Moreover,
characterizing agricultural land conversion trajectories from
remote sensing images is complex due to farm size, climatic
variability, changes in cropping patterns, and variations in the
rate of development processes. Here This work propose an
econometric time series approach to identify agricultural land loss
due to urban expansion, utilizing high temporal frequency MODIS
NDVI data between 2000 and 2010. The algorithm is comprised of two
main components: 1) detrending the time series, and 2) testing for
the presence of a breakpoint in the detrended time series and
estimating the date of the breakpoint. Evaluations of the algorithm
with simulated and actual MODIS NDVI data confirm that the method
can successfully detect when and where urban conversions of
agricultural lands occur. The algorithm is simple, robust, and
highly automated, thus is valuable for monitoring agricultural land
loss at regional and even global scalesAdvantages The proposed
method enables processing of very large datasets, either in spatial
extent or through time It reduces mistakes due to interpretation or
human error.Disadvantages This step-wise land-use transitions
result into deviation from the assumption of sequential phases in
land conversion process and limit the application of most change
detection algorithms.
CHAPTER 4CONCLUSION AND FUTURE WORK CONCLUSIONVisual object
recognition is an recent trend which is used to recognize the
objects visually through the systems. Currency recognition through
mobile phones will be a most effective methodology which will be
most useful for visually impaired persons. In this work, we have
ported the system to a mobile environment, working around like
limited processing power and memory, while achieving high accuracy
and low reporting time. Currency retrieval and thereafter
recognition is an example of fine-grained retrieval of instances
which are highly similar. Thus the result of our experimental
results proves that it is more robust to illumination changes than
the SIFT descriptor.FUTURE WORKThe system implemented in our work
is used to implement on Indian currency rupees whereas in further
research it can be implemented to support a world level currency
notes.
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