Land cover is one of basic data layers in geographic information system for physical planning and environmentalmonitoring. Digital image classification is generally performed to produce land cover maps from remote sensing data,particularly for large areas. In the present study the multispectral image from IRS LISS-III image along with ancillary datasuch as vegetation indices, principal componen...

Due to the low information content of individual SAR images, single-band SAR data do not provide highly accurate land cover classification. However, in areas under risk where rapid land cover mapping is required, the advantages of SAR which include cloud penetration and day/night acquisition, are evident in comparison to optical data. The main research goal of this study is to fuse different fr...

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize...

An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Artificial Neural Networks or simply Neural Networks are powerful general purpose computing tools. They have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more ac...

This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected...

The objective of this study is to develop land cover classification algorithm suited for the eastern Asia by using multi-temporal MODIS land reflectance products; Surface Reflectance 8-Day L3 product and Nadir BRDF-Adjusted Reflectance 16-Day L3 product. In this study, land cover maps derived from these two kinds of source data products are generated and compared. Time-domain co-occurrence matr...

Satellite based land cover classification for Africa’s semi-arid ecosystems is hampered commonly by heterogeneous landscapes with mixed vegetation and small scale land use. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. While most large scale land cover mapping attempts rely on moderate resolution data, PROBA-V prov...

in the recent years, human activities have led to changes in land use and land cover, consequently, these changes lead to the structure and function of ecosystems. spatial-temporal change detection of land use is important to understand the relationships and interactions between human and natural resources and to make appropriate decisions due to changes in land use and land cover occurs in bro...

This paper proposed a novel method of decision fusion based on weights of evidence model (WOE). The probability rules from classification results from each separate dataset were fused using WOE to produce the posterior probability for each class. The final classification was obtained by maximum probability. The proposed method was evaluated in land cover classification using two examples. The r...