Current study was conducted in order to finding the best models to estimating groundwater depth using Hyperion hyperspectral satellite imagery in the sugarcane fields located in the southwest of Iran. For this purpose ground water level was measured in 132 observation wells from the beginning of May 2010 till end of September 2010, twice per week, in the Hakim Farabi farming and industrial lands. Moreover, from the other collected information like daily weather information, age and variety of sugarcane, planting and harvesting date of plants, managerial operations such as date and amount of the fertilization, irrigation and drainage information in the Hakim Farabi farming and industrial lands were used. In a same time with measuring the ground data, a hyperspectral satellite image of Hyperion sensor was acquired on September 2, 2010. After applying necessary pre-processing on the image, the changes in the spectral reflectance of the sugarcane under different values of groundwater depths was studied. Afterwards, it was tried to obtain appropriate models for estimating ground water depth. For this purpose, capability of 21 vegetation indices, related to defferent regions of spectral reflectance of crops, was studied. Besides of these indices three new vegetation indices (SWSI-1, SWSI-2 and SWSI-3) were developed in this study. Results show that, variations of groundwater depths have a significant effect on spectral reflectance of sugarcane. Among the vegetation indices, indices related to water absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with groundwater depth. Obtained models from the two vegetation indices developed in this study (SWSI-1, SWSI-3) and NDWI yield the best results for estimating groundwater depth with R2 of 0.48, 0.48 and 0.47 and root mean square errors of 8.20, 8.25 and 7.98 cm respectively. Conclusions from this study indicate that using hyperspectral satellite imagery to monitoring water table in the sugarcane fields has an acceptable, fast and economical results.

The recent advances in hyperspectral remote sensing technology have provided a very good source of information for reconnaissance purposes. This is because of high spatial and spectral resolutions of airborne hyperspectral imaging sensors, and also the use of spectral signature of phenomena and objects for identification of military targets. In this paper, the ability of hyperspectral imagery for detection of targets is investigated by providing two important analyses including anomaly detection and target recognition. Besides, in a case study, this ability is evaluated practically by performing the algorithms on a real hyperspectral image from the HyMap sensor. Afterwards, two criteria are presented for evaluation and control of hyperspectral camouflage and deception techniques. Finally, assuming consideration of the indirect measures of passive defence, the essential considerations of CC& D (Camouflage, Concealment, and Deception) measures are presented against the threats of hyperspectral imagery.

Feature selection (FS) for target detection (TD) attempts to select features that enhance the discrimination between the target and the image background. Moreover, TD usually suffers from background interference. Therefore, features that help detectors suppress the background signals and magnify the target signal effectively are considered more useful. Accordingly, in this paper, a supervised FS method, called autocorrelation-based feature selection (AFS), is proposed based on the TD concept. This method uses the image autocorrelation matrix and the target signature in the detection space (DS) for FS. Features that increase the first-norm distance between the target energy and the mean energy of the background in DS are selected as the optimal features. To evaluate the proposed method and to explore the impact of FS on the TD performance, the target detection accuracy (TDA) measure is employed. The experiment shows that the proposed FS method outperforms the two existing FS methods used for comparison. In fact, AFS achieves the maximum TDA value of 19. 02% using 58 features while, compared to FS, the other methods achieve much lower values. Furthermore, the effect of image partitioning on the TD performance in both full-band and reduced-dimensionality feature spaces is investigated. The experiment results show that partitioning, as a way of adding local spatial information to TD, dramatically improves the TD performance. For experiments, the HyMap dataset is employed.

Wetlands are one of the important types of ecosystems that play a fundamental role in the environment and provide significant benefits due to the resources that they contain. Therefore, it is necessary to monitor the changes in these ecosystems. The alterations in Earth’s ecosystems caused by the natural activities, such as drought, as well as human activities and population growth has been affecting the wetlands and waterbodies area. Therefore, for achieving a better detection of these changes over time, it is important to generate descriptive location maps based on the characteristics of wetlands. Hyperspectral images have shown potential use in many applications due to their high spectral resolution, and consequently, their high informative value. This study presents a hybrid procedure for automatic detection of changes in wetlands using a new approach which can provide more details about the changes with high accuracy. The hybrid proposed method is based on incorporating chronochrome, Z-score analysis, Otsu algorithm, simplex via split augmented lagrangian (SISAL), Harsanyi–Farrand–Chang (HFC), Pearson correlation coefficient (PCC), and support vector machine (SVM) to detect changes using hyperspectral imagery. The proposed method in the first step, produce a training data for tuning SVM and kernel parameters. The second step, predicted change areas based on a chronochrome algorithm and binary change map obtained using SVM classifier. The third step, the amplitude of changes is created by Z-Score analysis and binary change mask. Finally, the multiple change map is produced based on the estimation of number and extraction of endmembers and similarity measure. The proposed method evaluated and compared the performances with other common hyperspectral change detection methods using three real-world datasets of multi-temporal hyperspectral imagery. The empirical results reveal the superiority of the proposed hybrid method in extracting the change map with an overall accuracy of nearly 96% and a kappa coefficient of 0.89 while other hyperspectral change detection methods have the overall accuracy lower than 93% and kappa coefficient 0.80. In addition, this hybrid method can provide ‘multiple changes’ as well as the magnitude of extracted changes.

High spectral resolution of hyperspectral images in form of very narrow and constant within visible and infrared spectral ranges has brought the technology of remote hyperspectral measurement into spotlight in order to detect objects as well as earthly phenomena. In this field، most methods have been presented with the purpose of improving the accuracy of image classification and there have been scarce researches on target detection (TD). Supervised TD problem can be considered as a one-class classification problem between the target and non-target pixels using training data from the target class only. However، a spectral signature of the target sample obtained using field or laboratory measurements is the only training data available for TD. A substantial number of bands lead to heavy computational costs along with Hughes Effect on hyperspectral image processing. Hence، in recent years much attention has been paid to reduction of computational complexity in the processing of hyperspectral images. In comparison to the classification field، few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. A chief reason behind this is the shortage or absence of training samples of the desired target in background images. In order to solve this problem، a method is introduced based on target simulation. Recently، target simulation method has been used for creating artificial sub-pixel targets on hyperspectral images in order to investigate the performance of sub-pixel target detection (STD) algorithms. But in this paper target simulation has been used as method for optimum band selection. In this method for STD several simulated training samples، created by means of target spectrum implantation in the image. For optimal band selection after achieving sufficient implanted targets as training data، searching strategy in hyperspectral image space is of high account. Once simulated targets are created، optimal bands are selected via Particle Swarm Optimization (PSO) Algorithm. To make the optimization algorithms exploit well in search space، their cost functions must be well defined. So one of contributions of this study is defining a new cost function for optimization algorithms used for selecting optimal bands. In the next stage، based on the optimal bands selected، the Local ACE is applied on the image to obtain detection result. ACE algorithm، one of the most useful and common algorithms for detection of sub-pixel and full pixel targets. But local ACE is commonly used to detect sub-pixel targets in hyperspectral images. In this version of the ACE algorithm، instead of using the mean and covariance of the entire image، just the neighbouring window pixels are used. The output of this stage will be TD map that by applying a threshold in a detection map can determine whether the pixels are target or not. In order to evaluate and study the ability of the introduced algorithm، Target Detection Blind Test (TDBT) of Hymap dataset and Hyperion dataset from Botswana have been used. False alarm rate was used as touchstone to evaluate the results between the outputs of different methods. Compared to some PSO-based algorithms such as maximum-submaximum-ratio (MSR) and correlation coefficient (CC)، an evolutionary method such as genetic algorithm (GA)، and the use of full band، the proposed method was able to improve the results by 46% in all cases where it was possible to decrease false alarm for the searched target.

Noise removing and radiometric correction is one of the challenging in Hyperspectral image processing. The one of these errors is striping which is presented in most of the remote sensing imagery. The destriping methods include statistical and filtering approaches. In the most of these algorithms, the structural information also removed after destriping. The presented method is combined wavelet-FFT filter in order to remove stripe artifact problem. In the first step, the original image is wavelet decomposed and subsequently, the bands containing the stripe information (vertical detail) are FFT transformed to remove the stripe errors. The visual assessments, as well as quantitative estimation of energy loss of the result show the capabilities and the performance of the purposed method in order to destriping. Also the result shows all structural features, which are different from stripes are optimally preserved and despite the statistical methods, the purposed algorithm doesn’t need the neighborhood information.

Hyperspectral remote sensing technology has witnessed remarkable progress in the last two decades. One of the analyzes performed on the hyperspectral images is target detection. In this research, the detection of roofs with special cover has been done as a target in an urban environment. Simultaneously with the growth of urbanization and the development of urban areas, the need of managers and planners for very accurate maps of urban areas has increased significantly. Since an urban environment has complex characteristics in terms of physical, geometrical and elements used in buildings, hyperspectral data effectively help to identify, extract and produce a map of the constituent elements of an urban environment. Regarding the spectral detection of the target, continuous and numerous researches have been carried out since the last two decades. According to the studies carried out, until now, the hierarchical algorithm has achieved the best results in comparison with other algorithms for extracting spatial information in hyperspectral images, Therefore, in this research, it is tried to reveal buildings with special cover in hyperspectral images by presenting a new and accurate method. Material and methods: The image data of the CASI sensor has been used to carry out this research. The images processed in this research include images with 32 spectral bands and a resolution of 2 meters, which were taken in May 2001 from the urban area of ​​Toulouse located in the south of France. In the proposed method, two classification algorithms of multilayer perceptron neural network (MLP) and support vector machine (SVM) are implemented on the hyperspectral image. Then, the map resulting from the combination of the two mentioned algorithms is used to select the marker for the marker-based hierarchical segmentation algorithm. Finally, with the help of the majority vote decision rule, the marker-based hierarchical segmentation map is combined with the map resulting from the integration of MLP and SVM classifications. Results and discussion: In this research, Gaussian radial basis kernel was used to implement the SVM algorithm. The values ​​of two parameters, penalty (C) and width of Gaussian function () were determined in SVM algorithm with the help of cross validation technique. The MLP classification algorithm was implemented with 3 hidden layers that include 5, 6 and 8 neurons and its evaluation was done with 500 repetitions and to select markers, the analysis of the labeling of connected components was done based on 8 neighborhood pixels on the map resulting from the combination of MLP and SVM. Based on the obtained results, the map obtained from the proposed method includes uniform regions and has more interconnected structures to reveal buildings, which shows the importance of using spatial information along with spectral information. Conclusion: In this research, the strategy of using spatial information along with spectral information to improve target detection in the analysis of hyperspectral images was examined. For this purpose, the spectral-spatial marker-based hierarchical algorithm, which is used in the image classification process, was used to reveal the roofs of the buildings. In the proposed method, two classification maps were used in the selection of markers and the decision rule of the majority vote in the case of the initial hierarchical segmentation algorithm. In the combination of MLP and SVM classification maps, conditional probability and selection of the highest probability of each pixel belonging to a class are used in the selection of markers and majority vote decision rule. Material and methods: The image data of the CASI sensor has been used to carry out this research. The images processed in this research include images with 32 spectral bands and a resolution of 2 meters, which were taken in May 2001 from the urban area of ​​Toulouse located in the south of France. In the proposed method, two classification algorithms of multilayer perceptron neural network (MLP) and support vector machine (SVM) are implemented on the hyperspectral image. Then, the map resulting from the combination of the two mentioned algorithms is used to select the marker for the marker-based hierarchical segmentation algorithm. Finally, with the help of the majority vote decision rule, the marker-based hierarchical segmentation map is combined with the map resulting from the integration of MLP and SVM classifications.Results and discussion: In this research, Gaussian radial basis kernel was used to implement the SVM algorithm. The values ​​of two parameters, penalty (C) and width of Gaussian function () were determined in SVM algorithm with the help of cross validation technique. The MLP classification algorithm was implemented with 3 hidden layers that include 5, 6 and 8 neurons and its evaluation was done with 500 repetitions and to select markers, the analysis of the labeling of connected components was done based on 8 neighborhood pixels on the map resulting from the combination of MLP and SVM. Based on the obtained results, the map obtained from the proposed method includes uniform regions and has more interconnected structures to reveal buildings, which shows the importance of using spatial information along with spectral information.Conclusion: In this research, the strategy of using spatial information along with spectral information to improve target detection in the analysis of hyperspectral images was examined. For this purpose, the spectral-spatial marker-based hierarchical algorithm, which is used in the image classification process, was used to reveal the roofs of the buildings. In the proposed method, two classification maps were used in the selection of markers and the decision rule of the majority vote in the case of the initial hierarchical segmentation algorithm. In the combination of MLP and SVM classification maps, conditional probability and selection of the highest probability of each pixel belonging to a class are used in the selection of markers and majority vote decision rule.