As an important application in remote sensing, landcover classification remains one of the most challenging tasks very-high-resolution (VHR) image analysis. rapidly increasing number Deep Learning (DL) based methods and training strategies are claimed to be state-of-the-art, already fragmented technical landscape mapping has been further complicated. Although there exists a plethora literature ...

In this article the eVectiveness of some recently developed genetic algorithm-based pattern classiŽ ers was investigated in the domain of satellite imagery which usually have complex and overlapping class boundaries. Landsat data, SPOT image and IRS image are considered as input. The superiority of these classiŽ ers over k-NN rule, Bayes’ maximum likelihood classiŽ er and multilayer perceptron ...

In this study we examine the use of Multiscale Fourier Transforms (MFT) on the effect of image fusion and segmentation. The proposed method takes SPOT-PAN and SPOT-XS images to register using MFT technique to produce high quality fused image. The algorithm works iterativly from coarsest level of decomposition to the top level. This process applied to the all sub-bands in order to give best choi...

Spatial data mining of Remotely Sensed Images (RSI) has become an important field of research as extremely large amounts of data are being collected from remote sources such as the Landsat satellite Thematic Mapper (TM) and other remote imaging systems. Association Rule Mining (ARM) has become an important method for mining large amounts of data in many areas beyond its originally proposed mark...

In the paper a number of original algorithms implemented as the program system are considered. The algorithms allow to perform the automated forecasting of land use/cover change using time series remotely sensed (RS) images. In the framework of the proposed approach the forecasting performs in two stages. In the first stage time series RS images are classified by the original classification alg...

In this paper, we describe our research in computer-aided image analysis. We have incorporated machine learning methodologies with traditional image processing to perform unsupervised image segmentation. First, we apply image processing techniques to extract from an image a set of training cases, which are histogram peaks described by their intensity ranges and spatial and textural attributes. ...