Ross F. Hayward

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors

This paper presents an evaluation of airborne sensors for use in vegetation management in power-line corridors. Three integral stages in the management process are addressed, including the detection of trees, relative positioning with respect to the nearest power line, and vegetation height estimation. Image data, including multispectral and high resolution, are analyzed along with LiDAR data captured from fixed-wing aircraft. Ground truth data are then used to establish the accuracy and reliability of each sensor, thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a pulse-coupled neural network and morphologic reconstruction applied to multispectral imagery. Through testing, it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved root-mean-square-error (rmse) values of 1.4 and 2.1 m for cross-track distance and along-track position, respectively, while direct georeferencing achieved rmse of 3.1 m in both instances. The estimation of pole and tree heights measured with LiDAR had rmse values of 0.4 and 0.9 m, respectively, while stereo matching achieved 1.5 and 2.9 m. Overall, a small number of poles were missed with detection rates of 98% and 95% for LiDAR and stereo matching.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Individual Tree Crown Delineation Techniques for Vegetation Management in Power Line Corridor

Remotely sensed, high spatial resolution images have great potential in assisting vegetation management in power line corridor. With the wide use of object-based approaches in remote sensing image analysis, individual tree crown delineation becomes a key research focus to improve the accuracy of plant information extraction. Although many algorithms have been investigated for individual tree crown delineation, no one algorithm seems suitable for all situations. As such, this paper investigates the applicability of several tree crown delineation techniques for complex environments in power line corridors. The advantages and limitations of these algorithms and prospective improvements are discussed. Initial experiment results on tree crown delineation employing JSEG are presented and compared with Mats Eriksonpsilas region-growing method.

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

A Biologically Inspired Object Spectral-Texture Descriptor and Its Application to Vegetation Classification in Power-Line Corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this letter, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of images or objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSFs) of a pulse-coupled neural network, which is invariant to rotation, translation, and small scale changes. The proposed method is first evaluated in a rotation- and scale-invariant texture classification using the University of Southern California Signal and Image Processing Institute texture database. It is further evaluated in an application of vegetation species classification in power-line corridor monitoring using airborne multispectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective in representing the spectral-texture patterns of objects, and it shows better results than classic color histogram and texture features.

The truth is in there: current issues in extracting rules from trained feedforward artificial neural networks

A recognized impediment to the more widespread utilization of artificial neural networks (ANNs) is the absence of a capability to explain, in a human-comprehensible form, either the process by which a trained ANN arrives at a specific decision/result or, in general, the totality of knowledge embedded therein. There has been a proliferation of techniques aimed at redressing this situation and, in particular, for extracting the knowledge embedded in trained feedforward ANNs as sets of symbolic rules. However, if the dissemination of ideas in the field of ANN rule extraction is to proceed in a systematic manner, then it is essential that a rigorous taxonomy exists for categorizing the plethora of techniques being developed. This paper shows how one of the proposed schemas for categorizing ANN rule extraction techniques is able to accommodate such developments in the field. In addition attention is drawn to what are seen to be some of the key challenges in the area including the identification of factors which appear to limit what is actually achievable through the rule extraction process.

A template matching table for speeding-up game-tree searches for hex

Transposition tables have long been a viable tool in the pruning mechanisms of game-tree search algorithms. In such applications, a transposition table can reduce a game-tree to a game-graph with unique board positions at the nodes. This paper proposes a transposition table extension, called a template matching table, where templates that prove winning positions are used to map features of board positions to board values. This paper demonstrates that a game-tree search for the game of Hex can have a more effective pruning mechanism using a template matching table than it does using a transposition table.