The vast amount of data collected by satellites via remote sensing is a valuable resource, however, it lacks machine search capabilities. In particular, large land cover datasets, such as the 30-m/cell NLCD 2006 covering the entire conterminous United States, are rarely analyzed as a whole due to the lack of tools beyond the basic statistics and SQL queries. Consequently, the NLCD is underutilized relative to its potential. We address this issue by introducing LandEx—a GeoWeb application for real time, content-based exploration and mining of land cover pattern sin large data sets . By combining the functionality of online computerized maps with the power of the pattern recognition algorithm, LandEx provides an easy to use visual search engine for the entire extent of the NLCD at its full resolution. The user selects a pattern of interest (aquery) and the tool produces a similarity map indicating the spatial distribution of locations having patterns of land cover similar to that in the query. Pattern-based query and retrieval addresses the issue of structural similarity between landscapes. The core of the method is the similarity function between two patterns which is based on 2D land cover class/clump size histograms and the Jensen-Shannon divergence. The search relies on exhaustive evaluation using an overlapping sliding window approach. LandEx is implemented using Free Open Source Software (FOSS) software and adheres to the Open Geospatial Consortium (OGC) standards. The wait time for an answer to a query is only several seconds due to the high level of system optimization. The methodology and implementation of LandEx are described in detail and illustrative examples of its application to different domains, including agriculture, forestry, and urbanization are given. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/

1. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS
AND REMOTE SENSING, VOL. 7, NO. 1, JANUARY 2014”
LandEx—A Geo Web Tool for Query and Retrieval of Spatial Patterns in Land Cover Datasets”

2. Abstract
 The vast amount of data collected by satellites via remote sensing is a valuable
resource, however, it lacks machine search capabilities. In particular, large land cover
datasets, such as the 30-m/cell NLCD 2006 covering the entire conterminous United
States, are rarely analyzed as a whole due to the lack of tools beyond the basic
statistics and SQL queries. Consequently, the NLCD is underutilized relative to its
potential.
 We address this issue by introducing LandEx—a GeoWeb application for real time,
content-based exploration and mining of land cover pattern sin large data sets .
 By combining the functionality of online computerized maps with the power of the
pattern recognition algorithm, LandEx provides an easy to use visual search engine for
the entire extent of the NLCD at its full resolution. The user selects a pattern of
interest (aquery) and the tool produces a similarity map indicating the spatial
distribution of locations having patterns of land cover similar to that in the query.
 Pattern-based query and retrieval addresses the issue of structural similarity between
landscapes. The core of the method is the similarity function between two patterns
which is based on 2D land cover class/clump size histograms and the Jensen-Shannon
divergence.
 The search relies on exhaustive evaluation using an overlapping sliding window
approach. LandEx is implemented using Free Open Source Software (FOSS) software
and adheres to the Open Geospatial Consortium (OGC) standards.
 The wait time for an answer to a query is only several seconds due to the high level of
system optimization. The methodology and implementation of LandEx are described
in detail and illustrative examples of its application to different domains, including
agriculture, forestry, and urbanization are given

3. Existing System
 In the existing system the Landsat images was generally used
to monitor crop condition , yield estimates, forest fire
detection, land cover change mapping analysis alone.
 Medium resolution sensors were used in the existing
approach which have an ideal spatial resolution for
vegetation mapping at the field scale in order to predict the
satellite detected images.
 The captured images in the urban areas were so very cloudy
and with so many disturbances to capture, so in our system
we fails to identify the clarity of images.. (Apart from that the
urban areas tends to opt for more spatial resolution
 Landsat scenes are about 35% cloud covered on average
globally and probability of taking two cloud-free
observations of a Landsat images at southern Asia within 48
days is less than 60%
 Landsat is limited by a 16-day revisit cycle and this was made
worse by cloud contamination in those images

4. Proposed System
 A possible solution for applications that require fine spatial
resolution (The spatial and temporal adaptive reflectance
fusion model) STARFM was introduced.
 STARFM model blends Landsat and MODIS data to
generate synthetic “daily” surface reflectance products at
Landsat spatial resolution .It requires a minimum of two
image pairs as the inputs into the algorithm.
 The STARFM approach can work with one image pair,
which is a more flexible approach for cloudy regions where
finding cloud-free Landsat scenes are very scarce.
 The one image pair detection is useful in forward
prediction of Landsat imagery because new MODIS data
are available throughout the growing season.

5. SYSTEM ARCHITECTURE
 HARWARE REQUIREMENT:
Processor : Core 2 duo
Speed : 2.2GHZ
RAM : 2GB
Hard Disk : 160GB
 SOFTWARE REQUIREMENT:
Platform : DOTNET (VS2010) , ASP.NET
Dotnet framework 4.0
Database : SQL Server 2008 R2

6. Architecture Diagram