REMOTELY SENSED IMAGE FAST CLASSIFICATION AND SMART THEMATIC MAP PRODUCTION

Apps available for Smartphone, as well as software for GNSS/GIS devices, permit to easily mapping the localization and shape of an area by acquiring the vertices coordinates of its contour. This option is useful for remote sensing classification, supporting the detection of representative sample sites of a known cover type to use for algorithm training or to test classification results. This article aims to analyse the possibility to produce smart maps from remotely sensed image classification in rapid way: the attention is focalized on different methods that are compared to identify fast and accurate procedure for producing up-to-date and reliable maps. Landsat 8 OLI multispectral images of northern Sicily (Italy) are submitted to various classification algorithms to distinguish water, bare soil and vegetation. The resulting map is useful for many purposes: appropriately inserted in a larger database aimed at representing the situation in a space-time evolutionary scenario, it is suitable whenever you want to capture the variation induced in a scene, e.g. burnt areas identification, vegetated areas definition for tourist-recreational purposes, etc. Particularly, pixel-based classification approaches are preferred, and experiments are carried out using unsupervised (k-means), vegetation index (NDVI, Normalized Difference Vegetation Index), supervised (minimum distance, maximum likelihood) methods. Using test sites, confusion matrix is built for each method, and quality indices are calculated to compare the results. Experiments demonstrate that NDVI submitted to k-means algorithm allows the best performance for distinguishing not only vegetation areas but also water bodies and bare soils. The resulting thematic map is converted for web publishing.

Semi-Automatic Extraction of Rural Roads under the Constraint of Combined Geometric and Texture Features

The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent.

EXTRACTING FEATURE OF WATER FROM REMOTELY SENSED IMAGE

In the present work, different remote sensing techniques have been used to analyze remote sensing data spectrally using ENVI software. The majority of algorithms used in the Spectral Processing can be organized as target detection and classification. In this paper method of target detection has been studied constrained energy minimization on the Therthar Lakeand surrounding area has been done. Also the results that obtained from applying constrained energy minimization were more accurate than other method comparing with the real situation.

Remotely Sensed Image Based on Robust Segmentation and GIS System

The continuous rising abstraction resolution of distant police work sensors sets new interest for applications victimization this information. For mining valuable information from far flung police work data, various classifiers hooked in to the supernatural examination of individual pixels are projected and big advancement has been accomplished. Even so, these methodologies have their restrictions, for the foremost half they manufacture "salt and pepper" boisterous outcomes. to beat such problems, object-arranged image examination strategy hooked in to multi-resolution division methodology was advanced and it's been used for various application functions effectively. During this examination, a productive remotely detected image smart understanding technique hooked in to image division and geographical information framework (GIS) was projected, within the 1st place, division hooked in to mean shift was utilized to amass the underlying parts from distant police work footage. At that time, apply vectorization (Raster to Vector Convertor) strategy to supply polygons from the divided image and highlight attributions, as an example, ghostly, shape, surface then on square measure removed by zonal investigation hooked in to distinctive formation and polygons. At last, creating getting ready take a look at and administered characterization square measure dispensed. just about all means that square measure accomplished in geo-data framework with the exception of image division. supported the investigation, we have a tendency to engineered up a product arrangement of remotely detected image examination. Contrasted and also the understanding methodology of a business programming eCognition, the projected one was gettable and practiced once applied to the Quick bird remotely detected footage.

Analysis of population dynamics of the regional unit of Chania using remote sensing and census data

Information about the size and the evolution of a place’s population has been sought from ancient times. That kind of information can be secured by periodic census enumerations. The Hellenic Statistical Authority provides data about the population and social conditions, also economical indices for each economic sector and the industrial trade. The study area, the regional unit of Chania, is in the island of Crete and the population resides mostly in the lowlands. In order to study the density, distribution and evolution of the population, many quantitative geographical methods were used, such as the Location Quotient (LQ), the Coefficient of Specialization (CS), the Coefficient of Localization (CL) and the Gini – Hirschman Index. The dataset chosen to execute the models, is derived from the Hellenic Statistical Authority’s website, for the years 2001 and 2011, the most recent census available data and is free of charge. To examine the distribution and the density of the population in accordance with the urban sprawl in the area the results were correlated with remotely sensed image. To do so, the Built – up index was calculated and for each municipality in the county and the mean value was used in a linear regression model with the population. In conclusion, the analysis combines the results of the quantitative methods for the productive sectors, the population density and growth with the urban sprawl, to examine the way the population of the county evolved.