High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve ...

In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. Image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. The features were fed into the neural network system as the recognition criteria and inputs. Multilayer perceptron (MLP) ne...

Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation skin lesions necessary to assess characteristics disease; however, it limited by long timelines and variety in interpretation. As early accurate diagnosis SC crucial increase patient survival rates, machine-learning (ML) deep-learning (DL) approaches have been developed overcome these issues support dermatologi...

As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introduc...

In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (...

This paper describes the fusion of information extracted from multispectral digital aerial images for land use classification. The proposed approach integrates spectral classification techniques and spatial information. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and NIR channels and form the basis for information ext...

High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of environment in that can be discretized categories during a classification process. Currently, there is an increasing use supervised machine learning (ML) classifiers retrieve accurate results using scarce datasets with samples non-linear relationships. ...

In many publications the performance of different classification algorithms regarding to agricultural classes is evaluated. In contrast, this paper focuses on the potential of different imagery for the classification of the two most frequent classes: cropland and grassland. For our experiments three categories of imagery, high resolution aerial images, high resolution RapidEye satellite images ...