Tuan Ab Rashid Bin Tuan Abdullah

Scene classification for aerial images based on CNN using sparse coding technique

ABSTRACT Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery (HRRS). Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network (CNN) approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle (UAV) and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features (multiple scales) from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low- and mid-level features, exhibiting an overall 90.3% accuracy rate for scene classification compared to 85.4% achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging.

Measuring Output Multipliers of Energy Consumption and Manufacturing Sectors in Malaysia during the Global Financial Crisis

Abstract The strong relationship between energy consumption and economic growth is widely recognized. Most countries’ energy demand declined during the economic depression known as the Global Financial Crisis (GFC) of 2008–2009. The objective of the current study is to investigate the energy consumption and performance of Malaysias manufacturing sectors during the GFC. We applied the output multiplier approach, which is based on the input-output model. Two input-output tables of Malaysia covering 2005 and 2010 were used. The results indicate significant changes in the output multipliers of the manufacturing sectors between 2005 and 2010. Moreover, the energy-to-manufacturing sectors’ output multipliers also decreased during the GFC due to a decline in export-oriented industries during the crisis. The increasing importance of the manufacturing sector to the development of Malaysian trade resulted in a noticeable decrease in the consumption of each energy sectors output, especially the electricity and gas sector. Based on the research findings, it is very important to have proper planning in manufacturing sector to reduce high import dependence, shortages of skilled labor, lack of competitiveness and limited indigenous technological capabilities.

Design of dictionary based on Discrete Tchebichef Transform

In this paper, design of overcomplete dictionaries based on Discrete Cosine Transform and Discrete Tchebichef Transform using optimization techniques is presented. Further, we optimized our proposed dictionaries based on KSVD algorithm and measured the performance of dictionaries using orthogonal matching pursuit (OMP) and basis pursuit (BP). The result showed that the Dictionary based on Discrete Tchebichef Transform (DTT) performed better as compared to the dictionary based on Discrete Cosine Transform (DCT). The proposed transform is first time introduced to made comparison with the DCT based dictionary generation. The proposed dictionaries are predetermined and optimize using KSVD algorithm. The accuracy will be increase with the slight increase of the computation complexity using Discrete Tchebichef Transform as compared to the Discrete Cosine Transform. The root mean square values are used to measure the accuracy.

Designing of disparity map based on hierarchical dynamic programming using satellite stereo imagery

Vegetation encroachment and its monitoring near high voltage power line is a challenging problem for electricity distribution companies. Electric supply companies monitor the vegetation/trees near power lines to avoid the blackouts that may occur in case of an improper monitoring of vegetation. The uninterrupted electric power supply is vital for industries, businesses, and daily life. Many approaches are employed to monitor vegetation/trees near the transmission line poles, but these approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation near or under the power poles using satellite stereo images which were acquired using QuickBird and IKONOS satellite images and calculated disparity map using stereo matching algorithms i.e. dynamic programming (DP) and Hierarchical dynamic programming (HDP). The 3D depth of vegetation is based on disparity map which has been measured using stereo algorithms. Results showed that HDP performs well as compared to DP based on QuickBird and IKONOS satellite stereo images in terms of accuracy.

Designing of overcomplete dictionaries based on DCT and DWT

Sparse representation is very active area in computer vision and image analysis. It has many applications in de-noising, stereo vision, image painting, image restoration, image de-blurring and many. For sparse modeling, there is need to design an appropriate dictionary. However, there are many dictionaries used for sparse modeling and were reported in literature. In this paper, we implemented the fixed dictionaries and adaptive dictionaries i.e., Method of Optimal Direction (MOD) and KSVD. Both adaptive are used for training the noisy images and computing the error and recovered the number of atoms using adaptive or small patches of images. The result showed that our proposed dictionaries performed much better for atom recovery in noisy patches of the images. The dictionary based on discrete wavelet transform (DWT) basis function with KSVD produced accurate result as compared to all other dictionaries. However, for fast convergence of RMSE value to minimum, DWT with KSVD and MOD dictionaries showed higher convergence rate as compared to discrete cosine transform (DCT) with KSVD and MOD. The computation complexity increased little using the DWT dictionary as compared to DCT dictionary.

Dynamic programming based comparison including QuickBird and IKONOS satellite stereo images for monitoring vegetation near power poles

Vegetation encroachment under overhead high voltage power line and its monitoring is a challenging problem for electricity distribution companies. Blackout can occurs if proper monitoring of vegetation is not done. The uninterrupted electric power supply is vital for industries, businesses, and daily life. Therefore, it is mandatory for electricity companies to monitor the vegetation/trees near power lines to avoid the blackouts. Many approaches are employed to monitor vegetation/trees near the transmission line poles, but these approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation near or under the power poles using satellite stereo images which were acquired using QuickBird and IKONOS satellites. 3D depth of vegetation has been measured using stereo algorithm incorporating dynamic programming. We have also compared the results of QuickBird and IKONOS satellite stereo images. Results showed that QuickBird satellite image performs well as compared to IKONOS using stereo vision global optimization dynamic programming algorithm in terms of accuracy and speed.

Monitoring of vegetation near power lines based on dynamic programming using satellite stereo images

Vegetation encroachment of high voltage power line endorsement space is an exceeding problem for electricity distribution companies. The electrical utilities have responsibility to impose their vegetation management proceeding so as to evade vegetation/ trees near transmission power lines. When the height of trees/vegetation is increased and it makes a contact with the power lines, it may antecedent the power lines in result of blackouts. Blackouts come about due to vegetation encroachments can cause impressive compensation. The uninterrupted electric supply is very imperative for industries, businesses, and populous areas. It is indispensable for electricity companies to monitor the vegetation/trees near power lines, There are many approaches applicable to monitor vegetation/trees near the transmission line poles, but these approaches are time inefficient in terms of time and finance. In this paper, a novel technique for depth estimation of vegetation/trees is proposed. In the study, Dynamic Programming is employed on stereo satellite images to determine depth of vegetation and trees. The experimental results on QuickBird imagery exhibit that the proposed technique performs better compared to block matching technique in terms of accuracy.

Power LinesVegetation enchroachment monitoring based on Satellite Stereo images using stereo matching

Continuous monitoring the vegetation encroachment for high voltage transmission lines is challenging task for electrical distribution authorities. The enchroachment causes interruption which results in blackouts. It also endures great cost to the authorities for maintenance and damage compensation. There are many methods available to monitor growth of vegetation near transmission line poles. However, these methods are constrained with time consumption and cost. In this paper,a new method based estimating 3D height/depth of vegetation or trees near transmission lines using satellite stereo vision is proposed. We have evaluated local methods of stereo matching using Quickbird Satellite stereo images and compare the performance of stereo matching cost functions in terms of processing speed and time. Results show that our proposed algorithm is faster and more accurate than the existing algorithm for disparity calculation.

Development of a facility for an advance competency in Electrical Engineering

This paper discussed the development of a facility for an advance competency in Electrical Engineering. The development was to fulfill the goal of the engineering education in an engineering college. The facility provided a facility to perform electrical network analysis, an industry size electrical network database and electronics communication devices. Firstly, this paper describes the methodology in formulating the advance competency and followed by a review on fundamental components of advance competency in electrical engineering including the network analysis, construction management and maintenance management. Subsequently, this paper presented a proposed method to assess the level of competency in engineering based on a supervised learning, where students worked to solve themes relevant to industrial problems. The problem is modeled by the test network in a database and electronic signals from the communication devices. Then the solution is formulated and tested based on the network analytical tools such as load flow, contingency, fault analysis, construction project maps and maintenance analysis. In conclusion, this paper discussed the competency gained by the students which include the evaluation of engineering design, assets maintenance, construction management and optimized decision in plant life management. The facility benefits the Electricity Supply Industry community and enriches the tertiary educational programs by exposing students to relevant competency towards global sustainability through efficient electrical network.