Текущий контроль результатов изучения дисциплины
Примеры вопросов и заданий при защите лабораторных работ
Для подготовки заданий имеется архив из 52 статей на английском
языке.
ЛАБОРАТОРНАЯ 1.
Разговорная часть
1. Что обозначает временное, пространственное, спектральное и
радиометрическое разрешение космических снимков?
2. Опишите основные характеристики данных со спутника Landsat.
3. Опишите систему регистрации снимков Landsat WRS2.
4. Какая информация хранится в спецификации названий файлов,
предоставляемых USGS?
5. В чем состоит отличие данных, хранящихся в разных файлах
мультиспектрального снимка?
6. Расшифруйте название файла. Пример:
Entity
ID:
LT51560192011294KHC00
7. Опишите представленный ниже интерфейс выбора снимков.
8. Какие основные критерии нужно задавать при поиске и заказе
снимков на сайте http://earthexplorer.usgs.gov/
9. Какие дополнительные критерии можно задавать при поиске и
заказе снимков на сайте http://earthexplorer.usgs.gov/
10.Опишите, в чем различие данных, находящихся в наборах данных
L7 ETM+ SLC-off и L7 ETM+ SLC-on?
11.Оценить, сколько снимков Landsat 5 за период с 01.06.2007 по
30.08.2007 с облачным покрытием 0 % с Path=155 и Row=19 и
уровнем обработки L1 предоставляет сервис earthexplorer.usgs.gov
Задание 1. Перевод отрывка текста по теме лабораторной работы.
Пример 1.
L5 was developed by the National Aeronautics and Space Administration (NASA)
and launched in March 1984. After on-orbit checkout, it was initially operated by
the National Oceanic and Atmospheric Administration (NOAA). In September
1985, operation of L5 was turned over to a private company, Earth Observation
Satellite Company (EOSAT), now known as Space Imaging. In July 2001, the stilloperational L5 and its entire image archives were turned back over to the U.S.
Government to be operated by the U.S. Geological Survey (USGS). Over the
lifetime of L5, there have been three U.S. data product generation systems. The
initial processing system for L5 was the TM Image Processing System (TIPS). It
was used by NOAA, and later EOSAT adopted it when they assumed operational
control of the Landsat Program. EOSAT updated their processing system to the
Enhanced Image Processing System (EIPS) in October 1991. At the same time, the
USGS began its own TM archive, and it has always processed TM data with the
National Landsat Archive Production System (NLAPS).
Пример 2.
In general terms, an image is a digital picture or representation of an object.
Remotely sensed image data are digital representations of the Earth. Image data are
stored in data files, also called image files, on magnetic tapes, computer disks, or
other media. The data consist only of numbers. These representations form images
when they are displayed on a screen or are output to hardcopy. Each number in an
image file is a data file value. Data file values are sometimes referred to as pixels.
The term pixel is abbreviated from picture element. A pixel is the smallest part of a
picture (the area being scanned) with a single value. The data file value is the
measured brightness value of the pixel at a specific wavelength.
Raster image data are laid out in a grid similar to the squares on a checkerboard.
Each cell of the grid is represented by a pixel, also known as a grid cell. In
remotely sensed image data, each pixel represents an area of the Earth at a specific
location. The data file value assigned to that pixel is the record of reflected
radiation or emitted heat from the Earth’s surface at that location.
Пример 3.
Resolution is a broad term commonly used to describe:
• the number of pixels you can display on a display device, or
• the area on the ground that a pixel represents in an image file.
These broad definitions are inadequate when describing remotely sensed data. Four
distinct types of resolution must be considered:
• spectral—the specific wavelength intervals that a sensor can record
• spatial—the area on the ground represented by each pixel
• radiometric—the number of possible data file values in each band (indicated by
the number of bits into which the recorded energy is divided)
• temporal—how often a sensor obtains imagery of a particular area
These four domains contain separate information that can be extracted from the
raw data.
ЛАБОРАТОРНАЯ РАБОТА 2.
Разговорная часть
1. В чем состоит преимущество использования мультиспектральных
снимков?
2. Какие типы синтезирования цвета применяются для лучшей
визуальной интерпретации растительности по КС и почему?
3. Опишите полученные результаты слияния мультиспектральных
данных с панхроматическими данными.
Задание. Перевод отрывка текста по теме лабораторной работы.
Пример 1.
Landsat TM sensors have seven bands with a spatial resolution of 28.5 m. SPOT
panchromatic has one broad band with very good spatial resolution—10 m.
Combining these two images to yield a seven-band data set with 10 m resolution
provides the best characteristics of both sensors.
A number of models have been suggested to achieve this image merge. Welch and
Ehlers (1987) used forward-reverse RGB to IHS transforms, replacing I (from
transformed TM data) with the SPOT panchromatic image. However, this
technique is limited to three bands (R,G,B).
Пример 2.
Spectral calibrations between two sensors
The use of finer spatial resolution data is an essential step to integrate ground
measurements with coarse spatial resolution data. To perform this step, the
relationship of spectral responses was examined. We conducted internal spectral
calibrations between 30-m ETM+ and 1-km MODIS data due to the differences in
spatial and spectral resolutions between the two sensors. We examined the
correlations of blue, red, near infrared, middle infrared reflectance after finer
ETM+ data were aggregated to the same resolution (1-km) of MODIS data within
the area of ETM+ scene. The resulting spectral relationships for the identified
bands within the ETM+ scene were used for calibrating MODIS data across the
region. These bands are either being used directly as driving variables in the model
or indirectly for calculating various vegetation indices that drive the model. Such
calibrations are necessary because:
1) the models are established using ETM+ data, and
2) remotely sensed information between the 2 sensors is not identical, even for the
same targets.
ЛАБОРАТОРНАЯ РАБОТА 3.
Разговорная часть
1.Обоснуйте необходимость геометрической коррекции КС.
2.Как следует выбирать степень полинома при геометрической
коррекции КС?
3.Приведите требования в расстановке точек при геометрической
коррекции изображений.
4.Обоснуйте необходимый минимум наземных контрольных точек для
коррекции изображений с помощью полиномов 2 степени.
5.Прокомментируйте
точность
выполненной
привязки
с
использованием инструмента географического связывания.
6.Прокомментируйте
точность
выполненной
привязки
с
использованием инструмента «Прозрачность».
7.Прокомментируйте точность расстановки точек путем замены типа
точки.
Задание. Перевод отрывка текста по теме лабораторной работы.
Пример 1.
Raw, remotely sensed image data gathered by a satellite or aircraft are
representations of the irregular surface of the Earth. Even images of seemingly flat
areas are distorted by both the curvature of the Earth and the sensor being used.
This chapter covers the processes of geometrically correcting an image so that it
can be represented on a planar surface, conform to other images, and have the
integrity of a map. A map projection system is any system designed to represent
the surface of a sphere or spheroid (such as the Earth) on a plane. There are a
number of different map projection methods. Since flattening a sphere to a plane
causes distortions to the surface, each map projection system compromises
accuracy between certain properties, such as conservation of distance, angle, or
area. For example, in equal area map projections, a circle of a specified diameter
drawn at any location on the map represents the same total area. This is useful for
comparing land use area, density, and many other applications. However, to
maintain equal area, the shapes, angles, and scale in parts of the map may be
distorted (Jensen 1996). There are a number of map coordinate systems for
determining location on an image. These coordinate systems conform to a grid, and
are expressed as X,Y (column, row) pairs of numbers. Each map projection system
is associated with a map coordinate system.
Rectification is the process of transforming the data from one grid system into
another grid system using a geometric transformation. While polynomial
transformation and triangle-based methods are described in this chapter, discussion
about various rectification techniques can be found in Yang (1997). Since the
pixels of the new grid may not align with the pixels of the original grid, the pixels
must be resampled.
Resampling is the process of extrapolating data values for the pixels on the new
grid from the values of the source pixels. The pixels must be resampled to conform
to the new grid.
Пример 2.
Georeferencing refers to the process of assigning map coordinates to image data.
The image data may already be projected onto the desired plane, but not yet
referenced to the proper coordinate system. Rectification, by definition, involves
georeferencing, since all map projection systems are associated with map
coordinates. Image-to-image registration involves georeferencing only if the
reference image is already georeferenced. Georeferencing, by itself, involves
changing only the map coordinate information in the image file. The grid of the
image does not change. Geocoded data are images that have been rectified to a
particular map projection and pixel size, and usually have had radiometric
corrections applied. It is possible to purchase image data that is already geocoded.
Disk rectification involves:
• rearranging the pixels of the image onto a new grid, which conforms to a plane in
the new map projection and coordinate system
• inserting new information to the header of the file, such as the upper left corner
map coordinates and the area represented by each pixel
Пример3.
Usually, rectification is the conversion of data file coordinates to some other grid
and coordinate system, called a reference system. Rectifying or registering image
data on disk involves the following general steps, regardless of the application:
1. Locate GCPs.
2. Compute and test a transformation.
3. Create an output image file with the new coordinate information in the header.
Images can be rectified on the display (in a Viewer) or on the disk. Display
rectification is temporary, but disk rectification is permanent, because a new file is
created.
ЛАБОРАТОРНАЯ РАБОТА 4.
Разговорная часть
1. Для каких целей обычно применяют неконтролируемую
классификацию аэрокосмических снимков?
2. Опишите принципы работы алгоритма ISODATA.
3. В чем заключается основная особенность алгоритмов
неконтролируемой классификации в сравнении с контролируемой?
4. Постройте легенду тематической карты классификации породного
состава лесов на основании представленной ниже карты лесной
таксации и ее легенды. Обоснуйте выбор классов для легенды.
5. В каком случае полученные классы следует объединить?
Задание. Перевод отрывка текста по теме лабораторной работы.
Пример 1.
Unsupervised training is more computer-automated. It enables you to specify some
parameters that the computer uses to uncover statistical patterns that are inherent in
the data. These patterns do not necessarily correspond to directly meaningful
characteristics of the scene, such as contiguous, easily recognized areas of a
particular soil type or land use. They are simply clusters of pixels with similar
spectral characteristics. In some cases, it may be more important to identify groups
of pixels with similar spectral characteristics than it is to sort pixels into
recognizable categories.
Unsupervised training is dependent upon the data itself for the definition of classes.
This method is usually used when less is known about the data before
classification. It is then the analyst’s responsibility, after classification, to attach
meaning to the resulting classes (Jensen 1996). Unsupervised classification is
useful only if the classes can be appropriately interpreted.
Пример 2.
ISODATA Clustering Parameters
To perform ISODATA clustering, you specify:
• N - the maximum number of clusters to be considered. Since each cluster is the
basis for a class, this number becomes the maximum number of classes to be
formed. The ISODATA process begins by determining N arbitrary cluster means.
Some clusters with too few pixels can be eliminated, leaving less than N clusters.
• T - a convergence threshold, which is the maximum percentage of pixels whose
class values are allowed to be unchanged between iterations.
• M - the maximum number of iterations to be performed.
Пример 3.
Initial Cluster Means
On the first iteration of the ISODATA algorithm, the means of N clusters can be
arbitrarily determined. After each iteration, a new mean for each cluster is
calculated, based on the actual spectral locations of the pixels in the cluster, instead
of the initial arbitrary calculation. Then, these new means are used for defining
clusters in the next iteration. The process continues until there is little change
between iterations (Swain 1973).
The initial cluster means are distributed in feature space along a vector that runs
between the point at spectral coordinates (1-1, 2-2, 3-3, ... n-n) and the
coordinates (1+1, 2+2, 3+3, ... n+n). The initial cluster means are
evenly distributed between (A-A, B-B) and (A+A, B+B).
ЛАБОРАТОРНАЯ РАБОТА 5.
Задания и разговорная часть
Задание 1.
1. Оцените точность классификации по полученной матрице ошибок
классификации.
2. Оцените ошибки пропуска и добавления.
3. Рассчитайте индекс Каппа.
Задание 2.
1. Оцените разделимость обучающих выборок по трансформируемой
дивергенции.
2. Какие каналы наиболее подходят для классификации КС на основании
значений трансформируемой дивергенции.
Задание 3.
1. Оцените разделимость обучающих выборок по гистограммам в каждом
канале.
2. Какие каналы наиболее подходят для классификации КС на основании
построенных гистограмм?
Задание 4. Перевод отрывка текста по теме лабораторной работы.
Пример 1.
Supervised training requires a priori (already known) information about the data,
such as:
• What type of classes need to be extracted? Soil type? Land use? Vegetation?
• What classes are most likely to be present in the data? That is, which types of
land cover, soil, or vegetation (or whatever) are represented by the data?
In supervised training, you rely on your own pattern recognition skills and a priori
knowledge of the data to help the system determine the statistical criteria
(signatures) for data classification. To select reliable samples, you should know
some information – either spatial or spectral – about the pixels that you want to
classify.
The location of a specific characteristic, such as a land cover type, may be known
through ground truthing. Ground truthing refers to the acquisition of knowledge
about the study area from field work, analysis of aerial photography, personal
experience, etc. Ground truth data are considered to be the most accurate (true)
data available about the area of study. They should be collected at the same time as
the remotely sensed data, so that the data correspond as much as possible.
However, some ground data may not be very accurate due to a number of errors
and inaccuracies.
Пример 2.
A process is iterative when it repeats an action. The objective of the ERDAS
IMAGINE system is to enable you to iteratively create and refine signatures and
classified image files to arrive at a desired final classification. The ERDAS
IMAGINE classification utilities are tools to be used as needed, not a numbered
list of steps that must always be followed in order.
The total classification can be achieved with either the supervised or unsupervised
methods, or a combination of both. Some examples are below:
• Signatures created from both supervised and unsupervised training can be merged
and appended together.
• Signature evaluation tools can be used to indicate which signatures are spectrally
similar. This helps to determine which signatures should be merged or deleted.
These tools also help define optimum band combinations for classification. Using
the optimum band combination may reduce the time required to run a classification
process.
• Since classifications (supervised or unsupervised) can be based on a particular
area of interest (either defined in a raster layer or an .aoi layer), signatures and
classifications can be generated from previous classification results.
Пример 3.
Typically, the accuracy of a crisp land cover classification from remote sensing
images is assessed using error matrix, which is defined as cross-tabulation of pixels
of agreement and disagreement arranged in a two dimensional matrix of rows and
columns. The rows represent class allocation of pixels in classified image and
columns represent their corresponding class allocation as inferred from ground or
reference data albeit both are interchangeable. As the error matrix represents a
sample of testing pixels, its creation is highly dependent on a sound sampling
design procedure. The elements of error matrix are used to derive a number of
accuracy measures such as overall, user’s and producer’s accuracy, kappa
coefficient etc. The information contained in error matrix can also be used for
estimation of areas of land cover classes using any of the five techniques
implemented here.
Пример 4.
Traditional spectral classification of remotely sensed images applied on a pixel-bypixel basis ignores the potentially useful spatial information between the values of
proximate pixels. For some 30 years the spatial information inherent in remotely
sensed images has been employed, albeit by a limited number of researchers, to
enhance spectral classifcation. This has been achieved primarily by filtering the
original imagery to (i) derive texture `wavebands' for subsequent use in
classifcation or (ii) smooth the imagery prior to (or after) classifcation. Recently,
the variogram has been used to represent formally the spatial dependence in
remotely sensed images and used in texture classifcation in place of simple
variance filters. However, the variogram has also been employed in soil survey as a
smoothing function for unsupervised classifcation. In this review paper, various
methods of incorporating spatial information into the classifcation of remotely
sensed images are considered. The focus of the paper is on the variogram in
classifcation both as a measure of texture and as a guide to choice of smoothing
function. In the latter case, the paper focuses on the technique developed for soil
survey and considers the modifcation that would be necessary for the remote
sensing case.
Пример 5.
Empirical approaches
One of the simplest approaches to supervised classification is the minimumdistance-to-mean classifer, also referred to as the k-means or (here) c-means
classifer. This classifer utilises the Euclidean distances in (spectral) feature space
between (i) the pixels to be classifed and (ii) the class means (obtained from
training data).
In the univariate case, a training site is selected which belongs to a desired class
and the values for all pixels at the site are averaged to obtain the class mean. This
procedure is repeated for all other classes of interest. Each pixel in the remainder
of the image may then be allocated to the class mean to which it is nearest in
univariate feature space. In a simple empirical approach presented by Carr (1996)
this allocation is executed through the prior construction of a simple look-up table
such that any pixel value will have associated with it the destination class without
the need to compare the distances to all class means. This procedure is readily
extended to multivariate feature space where the distances to class means may be
obtained by Euclidean geometry.