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The purpose of this paper is to develop user friendly software with the minimum error and maximum performance in a form of remote sensing satellites evaluation software for estimation of weights and ranks of the remote sensing satellite plans, to decrease risk of management decisions.

The analytic hierarchy process (AHP) as a comprehensive framework for strategic decision making is used to arrive at the weights of criteria and sub‐criteria of remote sensing satellites. The Ms‐Access software is written to compute the ranks of the remote sensing satellite plans based on the relative weights of inputs and then, the outputs from AHP are shown as a numerical graph and generates the Ms‐Access database.

One of the main objectives of this paper is an attempt to access this skill that compare several remote sensing satellite plans on quantity and quality point of view by several effective criteria such as mass, power consumption and cost of satellites, in addition to the remote sensing subsystem, communication subsystem, telemetry, tracking and control subsystem, attitude determination control subsystem and their own sub‐criteria.

It is hard in just one paper, to gather lots of information about remote sensing satellite systems, use a new methodology that is unknown for aerospace engineering, and talk about an innovative software.

This paper provides helpful evaluating software which has a data bank that it is very useful and impartial advice for space strategy's managing organization to compare several plans.

This study provides low cost, time‐saving, and high‐performance remote sensing satellite evaluation software and gives valuable information and guidelines which help management decisions of aerospace organization.

The purpose of this paper is to establish the grey relational estimating model of soil pH value based on hyper-spectral data.

As to the uncertainty of the factors affecting the soil pH value estimation based on hyper-spectral, the grey weighted relation estimation model was set up according to the grey system theory. Then the linear regression correction model is established according to the difference and grey relation degree information between the estimated samples and their corresponding pattern. At the same time, the model was applied to Hengshan county of Shanxi province.

The results are convincing: not only that the linear regression correction model of grey relation estimating pattern of soil pH value based on hyper-spectral data is valid, but also the model’s estimating accuracy is higher, which the corrected average relative error is 0.2578 per cent, and the decision coefficient R²=0.9876.

The method proposed in the paper can be used at soil pH value hyper-spectral inversion and even for other similar forecast problem.

The paper succeeds in realising both the soil pH value hyper-spectral grey relation estimating pattern based on the grey relational theory and the correction model of the estimating pattern by using the linear regression.

This paper attempts to establish the grey GM(0,N) estimation model of the soil organic matter content spectral inversion under the uncertainties between soil organic matter contents and spectral characteristics and the theory of grey system.

At first, based on the uncertainty of the relationship between the soil organic matter content and spectral characteristics, using the ordered grey accumulation generation and grey GM(0, N) model to establish hyper‐spectral grey estimation model of soil organic matter content. Second, the presented model is used to estimate soil organic matter of Hengshan County in Shanxi province in the last part of the paper.

The results are convincing: not only that soil organic matter content spectral inversion grey GM(0, N) model based on the ordered grey accumulation generation theory is valid, but also the model's prediction accuracy is higher, with the sample's average prediction accuracy being 93.662 per cent.

The method exposed in the paper can be used on soil organic matter content hyper‐spectral inversion and even for other similar forecast problems.

The paper succeeds in realising both prediction pattern and application of soil organic matter content hyper‐spectral inversion by using the newest developed theories: grey GM(0, N) model based on the ordered grey accumulation generation.

Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR.

This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.

The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.

The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on.

A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

The selection of wavelength region and number of bands is a research problem for remote sensing experts for utilization of data provided by the sensor system. The present study proposes to make an evaluation for optimum band selection and classification accuracy.

The entropy, brightness value overlap index (BVOI), optimum index factor (OIF) and spectral separability analysis, i.e. Euclidean distance (ED), divergence, transformed divergence (TD) and Jefferies‐Matusita (JM) distance and accuracy of MLC classification were carried out. For the present study Terra ASTER, Landsat ETM⁺ and IRS 1D LISS III dataset has been used. The first three methods were for the spectral evaluation of the three satellite data used and for determination of information content, variance and spectral overlap among the classes present in the natural and man‐made landscape. The fourth method is for selection of spectral band combinations with highest separability of classes using divergence matrices. These band combinations are selected for the classification and subsequent accuracy assessment.

The OIF values are clearly indicating that the performance of ASTER data is the best, having the lowest correlation between the bands; hence the separability of the feature is also highest, while LISS III have shown high correlation between the bands, with the poor separability of the features. Landsat ETM⁺ data are in between these two sensors, better than LISS III but poorer than ASTER. The BVOI outputs of the three datasets of man‐made landscape show that band 3 of ASTER has the least overlap of the classes, followed by band 4 of ETM⁺. Very high overlap of the classes has been found in LISS III data. It has been found from spectral separability analysis of all the three datasets for the man‐made landscape that ASTER data with band combination of spectral bands 123468 contains the highest value of all the measures of spectral separability, i.e. ED (291.72), divergence (2,133.37), TD (2,000.00) and JM distance (1,414.10).

It can be inferred from the present study that spectral resolution plays a very important role in discrimination of vegetation features. ASTER data which are with the highest number of the bands amongst the satellite data used had shown highest classification accuracy, while LISS III data with lowest number of bands had shown lowest accuracy, and Landsat ETM⁺ stood in between the two sensors.

It is important to evaluate the sensor systems and their spectral regions for discrimination of vegetation features. The number of bands present in a particular sensor and the spectral regions used in it are some of the crucial factors which decide the usefulness of the data for different applications, including vegetation‐related studies. The selection of spectral wavelength region, i.e. spectral bands and the sensor system, presents the research problem for remote sensing experts to suggest the best spectral regions and satellite sensor for the discrimination of the vegetation features in different landscapes, namely man‐made and natural.

In the present study all the three datasets are extensively examined and tested for their vegetation discrimination capabilities using well‐established methodologies. All the parameters applied on the datasets revealed that spectral resolution definitely plays a role in the performance of the data as far as discrimination of features is concerned both in natural and man‐made landscape with desirable accuracy.

The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene.

In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity.

Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures.

The paper presents an effective retrieval method for hyperspectral images.

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

The purpose of this study is to present the state of the art for fiber Bragg grating (FBG) acceleration sensing technologies from two aspects: the principle of the measurement dimension and the principle of the sensing configuration. Some commercial sensors have also been introduced and future work in this field has also been discussed. This paper could provide an important reference for the research community.

This review is to present the state of the art for FBG acceleration sensing technologies from two aspects: the principle of the measurement dimension (one-dimension and multi-dimension) and the principle of the sensing configuration (beam type, radial vibration type, axial vibration type and other composite structures).

The current research on developing FBG acceleration sensors is mainly focused on the sensing method, the construction and design of the elastic structure and the design of a new information detection method. This paper hypothesizes that in the future, the following research trends will be strengthened: common single-mode fiber grating of the low cost and high utilization rate; high sensitivity and strength special fiber grating; multi-core fiber grating for measuring single-parameter multi-dimensional information or multi-parameter information; demodulating equipment of low cost, small volume and high sampling frequency.

The principle of the measurement dimension and principle of the sensing configuration for FBG acceleration sensors have been introduced, which could provide an important reference for the research community.