Hyperspectral Imaging Hyperspectral
imaging refers to the image of a scene over a large number of discrete,
contiguous spectral bands such that a complete reflectance spectrum can be
obtained.
One of the major hyperspectrometers is Airborne Visible/
Infrared Imaging Spectrometer (AVIRIS). AVIRIS was designed to image 224
contiguous bands in the region from 0.4 - 2.5 micro metres. The increased
spectral range in the visible region allows biologists to study important
reactions in the vegetation and shallow water biology. The resolution of
the system is of the order of 10 nm, providing sufficient resolution to
detect most absorption features. A few of the air-borne and space-borne
sensors are as given in Table 2.
Image Compression
Imagery data are voluminous and even in today’s fast
communicating world of the internet, it is difficult to transfer the image
data. Image compression technologies, however, now make it possible to
quickly move imagery via the Web. The latest technologies include Image
Pyramids, fractal and Wavelet Compression.
Image Analysis
TechniquesRecent analysis techniques are Image Fusion,
Interferometry, Decision Support Systems etc.
Image
FusionThe merging of multisensor data is becoming widely used
with diverse types of data as a result of improvements in terms of better
sensor resolution and rapid advances in computer image analysis. The
advancements in image analysis have allowed for greater.
Table 1: High Resolution Satellites
| Sensor |
Vis
bands |
IR
Bands |
PAN
Bands S |
Resolution
Visible (m) |
Resolution IR |
Swath |
Stereo
mode |
| QuickBird |
3 |
1 |
1 |
1 |
3.3 |
22 |
Yes |
| Ikonos |
3 |
1 |
1 |
1 |
3.3 |
12 |
Yes |
| Orbview-3 |
3 |
1 |
1 |
1 |
4 |
8 |
Yes |
| Eros |
|
|
1 |
1.5 |
|
13 |
Yes |
| RS PS |
|
|
1 |
2.5 |
|
30 |
Yes |
Table 2: Hyperspectral Imaging Sensors
| Sensor |
Spectral
bands |
Wavelength
range(nm) |
Bandwidth (Spectral
resolution)(nm) |
Swath
(Kms.) |
SNR |
PAN
Resolution(m) |
| AVIRIS |
224 |
400-2450 |
9.6 |
11 |
|
20 |
| COIS |
210 |
400-2450 |
10 |
30 |
>200 |
5 |
| Hyperion |
220 |
400-2500 |
10 |
7.5 |
|
10 |
| Warfighter |
200 |
400-2500 |
10 |
5 |
|
1 |
flexibility and
use of innovative techniques for combining and integrating
multi-resolution and multi -spectral data. The aims of image and data
fusion are to sharpen images, improve geometric corrections, provide
stereo-viewing capabilities for stereo-photogrammetry, to enhance certain
features not visible in either of the single data alone, detect changes
using multi-temporal data, substitute missing information in one image
with signals from another sensor image (e.g. clouds-VIR, shadows-SAR) and
to replace defective data.
Image fusion is performed at three
different processing levels: at pixel level, feature and decision levels.
Image fusion at pixel level means fusion at the lowest processing level
referring to the merging of measured physical parameters. Fusion at
feature level requires an extraction of objects recognised in the various
data sources. Decision-level fusion represents a method that uses
value added data where the input images are processed individually for
information extraction.
Radar InterferometrySince
the performance of optical sensors to generate digital elevation models is
somewhat less than desirable, the capabilities of coherent RADAR systems
have been explored to get inexpensive DEM data to the land areas of the
globe. ERS 1 and 2 as well as JERS -1 and Radarsat have not only become an
effective tool to image the earth’s surface through clouds and at night in
an "all weather system", due to the coherent nature of the active sensing
system, they have also permitted Radar Interferometry, which has the
potential of deriving a digital elevation model. Radar Interferometry is a
rapidly developing field in which two or more radar images of the same
location are processed together.
Decision Support
System(DSS)A Decision Support System is an interactive, flexible
and adaptable computer based information system, specially developed for
supporting the solution of a particular ill-structured problem for
improved decision making. It utilises data, it provides easy user
interface and it allows for the decision -maker’s own insights. Most
sophisticated DSS also utilises models, it is built by an iterative
process, it supports phases of decision making, and it includes
a knowledge base.