Recent developments in hyperspectral images have heightened the need for advanced classification methods. To reach this goal, this paper proposed an improved spectral-spatial method for hyperspectral image classification. The proposed method mainly consists of three steps. First, four band selection strategies are proposed to utilize the statistical region merging (SRM) method to segment the hyperspectral image. The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image. Finally, the final classification result is obtained using the decision fusion rule. Validation tests are performed to evaluate the performance of the proposed approach, and the results indicate that the new proposed approach outperforms the state-of-the-art methods.

Zelang Miao

Wenzhong Shi

A New Methodology for Spectral-Spatial Classification of Hyperspectral Images

This paper proposes a method to classify whether a landmark, which consists of the outline in a face shape model in the shape model based approaches, is properly fitted to feature points. Through this method, the reliability of information can be determined in the process of managing and using the shape. The enlarged face image by image sensor is processed by bilinear interpolation. We use the gray-value variance that considers the texture feature of skin for classification of landmarks. The gray-value variance is calculated in skin area of the patch constructed around the landmark. In order to make a system strong to poses, we project the image of face to the frontal face shape model. And, to fill out each area, the area with insufficient pixel information is filled out with bilinear interpolation. When the fitting is properly done, it has the variance with a low value to be calculated for smooth skin texture. On the other hand, the variance for misaligned landmark shows a high variance by the background and facial contour gradient. We have proposed a classifier using this characteristic and, as a result, classified the true and false in the landmark with an accuracy of 83.32&#x25; through the patch classifier.

Seokhoon Kang

Seonwoon Kim

Patch Classifier of Face Shape Outline Using Gray-Value Variance with Bilinear Interpolation

A compression technique for still digital images is proposed with deep neural networks (DNNs) employing rectified linear units (ReLUs). We tend to exploit the DNNs capabilities to find a reasonable estimate of the underlying compression/decompression relationships. We aim for a DNN for image compression purpose that has better generalization property and reduced training time and support real time operation. The use of ReLUs which map more plausibly to biological neurons, makes the training of our DNN significantly faster, shortens the encoding/decoding time, and improves its generalization ability. The introduction of the ReLUs establishes an efficient gradient propagation, induces sparsity in the proposed network, and is efficient in terms of computations making these networks suitable for real time compression systems. Experiments performed on standard real world images show that using ReLUs instead of logistic sigmoid units speeds up the training of the DNN by converging markedly faster. The evaluation of objective and subjective quality of reconstructed images also proves that our DNN achieves better generalization as most of the images are never seen by the network before.

Farhan Hussain

Jechang Jeong

Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons

Quantum particle swarm optimization (QPSO) is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

Zhehuang Huang

Improved Quantum Particle Swarm Optimization for Mangroves Classification

The problem, whose targets can not be effectively identified for airborne remote sensing images, is mainly due to the atmospheric scattering effect. This problem is necessary to be overcome. According to the statistical evaluations method and the different characteristics of polarization between the objects radiance and atmospheric path radiation, a new atmospheric correction method for airborne remote sensing images was proposed. Using this new method on the airborne remote sensing images which acquired on the north coast areas of China during the haze weather, we achieved a high quality corrected atmosphere-free image. The results demonstrate the power of the method on the harbor area. The results show that the algorithm, improving image contrast and image information entropy, can effectively identify the targets after atmospheric correction. The image information entropy was enhanced from 5.59 to 6.62. The research provides a new and effective atmospheric correction technical approach for the airborne remote sensing images.

Tianquan Liang

Xiaobing Sun

Han Wang

Rufang Ti

Cunming Shu

Airborne Polarimetric Remote Sensing for Atmospheric Correction

Based on land use classification system, this paper acquires the land use distribution status at 2000, 2005, and 2010 in Beijing-Tianjin-Tangshan Region using remote sensing images, field survey data, images in Google Earth, and visual interpretation methods. Then, the land use transformation rules from 2000 to 2010 are achieved using GIS (geographic information system) technology. The research results shows the following: (1) as to the distribution area of the land use types, dry field has the largest area, followed by forest land, building land, paddy field, water area, grassland, and unused land; (2) from 2000 to 2010, the area of building land has the largest increase, which is mainly transformed from cropland and sea reclamation area; the largest decreased land use type is paddy field, which mainly transforms to dry field and building land; (3) the high increase of building land and decrease of cropland suggest the land use transformation in the quick development process of economy; meanwhile, the total area of forestland and grassland changes little, so the ecological environment does not have apparent deterioration in the 1st decade of the new century.

Shang-min Zhao

Wei-ming Cheng

Hai-jiang Liu

Yao Xia

Hui-xia Chai

Yang Song

Wen-jie Zhang

Tian You

Land Use Transformation Rule Analysis in Beijing-Tianjin-Tangshan Region Using Remote Sensing and GIS Technology

This paper presents Kinect-based vision system of mine rescue robot working in illuminous underground environment. The somatosensory system of Kinect is used to realize the hand gesture recognition involving static hand gesture and action. A K-curvature based convex detection method is proposed to fit the hand contour with polygon. In addition, the hand action is completed by using the NiTE library with the framework of hand gesture recognition. In addition, the proposed method is compared with BP neural network and template matching. Furthermore, taking advantage of the information of the depth map, the interface of hand gesture recognition is established for human machine interaction of rescue robot. Experimental results verify the effectiveness of Kinect-based vision system as a feasible and alternative technology for HMI of mine rescue robot.

Wanfeng Shang

Xiangang Cao

Hongwei Ma

Hailong Zang

Pengkang Wei

Kinect-Based Vision System of Mine Rescue Robot for Low Illuminous Environment

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Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.

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Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.

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Journal of Sensors - Table of contents 2016

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Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.

Journal of Sensors