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...

Vehicle crashes are amongst the major causes of mortality and results in losses of lives and properties. A large number of the vehicle crashes occur on rural roads. Accidents become more noteworthy in two-lane roads due to going and coming traffic. Therefore, prediction of crashes and their causes are considerably important to reduce the number and severity of the accidents. The safety index is...

Figure 4. Example of a well pad & road network w/a high-pass filter over ETM imagery. The Southern Wind River Landscape (SWRL) is a three million acre area of Intermountain Semidesert in west central Wyoming. Within the three million acres lie both public and private land holdings. The Nature Conservancy (TNC) has significant conservation interests in this Landscape as well and has created a ne...

Classification is challenging task on complex features of Remote sensing satellite imageries color pixels variability of patterns. Machine learning techniques have delivered the improved in accuracy of classification of patterns of features. Remote sensing color based imageries having hard to cluster color pixels with variability in intensity of colors. Challenges in estimation of various featu...

One of the most important tasks in urban remote sensing is the detection of impervious surfaces (IS), such as roofs and roads. However, detection of IS in heterogeneous areas still remains one of the most challenging tasks. In this study, detection of concrete roof using an object-based approach was proposed. A new rule-based classification was developed to detect concrete roof tile. This propo...

The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel cla...

Using spectral-only information for landslides classification is usually confusing with houses, roads, and other bare lands because these ground features have similar spectral patterns on images. In this study, 3D airborne LiDAR data are integrated with SPOT images for landslide classification for improving classification accuracy. A study area is selected in a subbasin of Shimen Reservoir. SPO...

Landscape patterns are closely related to ecological processes. Different spatial scales and research methods may lead to different results. Therefore, it is crucial to choose suitable research methods when studying different landscape patterns and ecological processes. In the present study, the methods of source-sink landscape theory were applied to the interactions between urban landscape cha...

Interpreting laser data to allow autonomous robot navigation on paved as well as dirt roads using a fixed angle 2D laser scanner is a daunting task. This paper introduces an algorithm for terrain classification that fuses four distinctly different classifiers: raw height, step size, slope, and roughness. Input is a single 2D laser scan and output is a classification of each laser scan range rea...