The aim of this study was to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area using, (i) different classification algorithms with parameter optimization in some cases; (ii) different feature sets including spectral, textural and terrain features; and (iii) different seasonal combinations of images. A three-way ANOVA was used to discern ...

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectr...

This paper presents a novel approach to computer-supported diagnosing based on microscopic images of histological sections. A method of extraction of textural feature is presented, which is in a sense complementary to the texture-based segmentation. The textural feature is obtained by tracing the process of image segmentation. For classification, a n2-classifier oriented to multi-class problems...

In this contribution is investigated the use of multilayer perceptron type neural networks in the characterization of images by texture content. The paper is focused on the effects of textural feature extraction methods on the network architecture, training performance and generalization capability when applied in indexing of images contained within multimedia image databases. An in depth exper...

This contribution deals with the discrimination between stratiform and convective cells in meteorological radar images. This study is based on a textural analysis of the latter and their classification using a Support Vector Machine (SVM). First, we applied different textural parameters such as energy, entropy, inertia and local homogeneity. Through this experience, we identified the different ...

We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pléiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information...

This paper deals with processing of wireless capsule endoscopy (WCE) images from gastrointestinal tract, by extracting textural features and developing a suitable classifier to recognize as a normal or abnormal /tumor image. Images obtained from WCE are prone to noise. To reduce the noise, filtration technique is used. The quality of the filtered image is degraded, so to enhance the quality of ...

Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural fe...

nowadays, quality control of bread, especially in large scale production, requires application of novel methods. in this research, image analysis of digital images of barbary bread enriched with soy flour was carried out at two storage intervals. using imagej software, crumb features (mean cell area and void fraction) were computed for each image. fractal dimensions of bread crumb images were m...

This thesis describes the study of Artificial Neural Network (ANN) based techniques for the classification of aerial images for various types of land-use. In this study both gray-scale and multispectral aerial images were used in land-use classification. Three approaches were used for the preparation of the data as inputs to the ANN, including histograms of the pixel intensities, textural param...