Journal of Sensors 

About: Journal of Sensors is an academic journal published by Hindawi Publishing Corporation. The journal publishes majorly in the area(s): Computer science & Wireless sensor network. It has an ISSN identifier of 1687-725X. It is also open access. Over the lifetime, 3194 publications have been published receiving 35755 citations.

Deep Convolutional Neural Networks for Hyperspectral Image Classification

Abstract: Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

Significant remote sensing vegetation indices: A review of developments and applications

Abstract: Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV) Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas

A Review of Carbon Nanotubes-Based Gas Sensors

Abstract: Gas sensors have attracted intensive research interest due to the demand of sensitive, fast response, and stable sensors for industry, environmental monitoring, biomedicine, and so forth. The development of nanotechnology has created huge potential to build highly sensitive, low cost, portable sensors with low power consumption. The extremely high surface-to-volume ratio and hollow structure of nanomaterials is ideal for the adsorption of gas molecules. Particularly, the advent of carbon nanotubes (CNTs) has fuelled the inventions of gas sensors that exploit CNTs' unique geometry, morphology, and material properties. Upon exposure to certain gases, the changes in CNTs' properties can be detected by various methods. Therefore, CNTs-based gas sensors and their mechanisms have been widely studied recently. In this paper, a broad but yet in-depth survey of current CNTs-based gas sensing technology is presented. Both experimental works and theoretical simulations are reviewed. The design, fabrication, and the sensing mechanisms of the CNTs-based gas sensors are discussed. The challenges and perspectives of the research are also addressed in this review.

Surface Plasmon Resonance-Based Fiber Optic Sensors: Principle, Probe Designs, and Some Applications

Abstract: Surface plasmon resonance technique in collaboration with optical fiber technology has brought tremendous advancements in sensing of various physical, chemical, and biochemical parameters. In this review article, we present the principle of SPR technique for sensing and various designs of the fiber optic SPR probe reported for the enhancement of the sensitivity of the sensor. In addition, we present few examples of the surface plasmon resonance- (SPR-) based fiber optic sensors. The present review may provide researchers valuable information regarding fiber optic SPR sensors and encourage them to take this area for further research and development.

Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

Abstract: Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, for the first case, the standard deviation was 2.4°C, and for the second case, it was 2.7°C. For future studies, the tool should be refined with in situ measurements of land surface temperature.