Remote sensing data provides much essential and critical information for monitoring many applications such as image fusion, change detection and land cover classification. This paper proposed about the classification and extraction of spatial features in urban areas for high resolution multispectral satellite image. Spectral information is the foundation of remotely sensed image classification....

Received Dec 9, 2016 Revised Mar 27, 2017 Accepted Apr 11, 2017 The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multispectral image of Hyderabad and its surrou...

An important factor in sustainable agriculture and economic management is to calculate areas under different crops that the inputs of agriculture connect to this topic. Planning of agricultural mechanization, fertilizer and pesticide requirements, pests and diseases control, estimates of agricultural production, income and tax and financial planning, all linked to the cultivated areas and estim...

The objective of this study is to assess the accuracy of various satellite-derived land cover maps classified using a hybrid pixel-based and object-based image analysis technique. A 2006 QuickBird image covering a part of the eastern side of Tsukuba City, Japan, was used to derive the land cover maps. First, we classified the image using a pixel-based technique. Second, we generated five differ...

Satellite Radar Interferometry is a unique method that gives us a possibility to take measurements of a land subsidence on strictly determined huge areas and with precision state the time range. This method uses the phase signal difference between two SAR satellite images (Synthetic Aperture Radar). Until this time this method was successfully tested on underground coal mining areas in the Uppe...

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised c...

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classi...

The conventional method of compiling statistics on Crop Estimation is done by the Government Ministry to make ground surveys, during which selected farmers or village officials are interviewed regarding their crops. By comparison with the results of previous years, this information is then extrapolated to generate data and predictions on a regional basis. This means that these traditional surve...

-Land use classification is an important part of many remote-sensing applications. A lot of research has gone into the application of classifiers to remote-sensing images. Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover. In this paper, we have proposed an efficient technique for classifying the multispectral s...

In recent years, on average about 35% of cloud covers are generally present in optical satellite images. To develop cloud-free satellite images for analyses of current land cover and land-cover change cloud removal approach based on information cloning is introduced. The approach removes cloud-contaminated portions of a satellite image and then clones information from cloud-free patches to thei...