Fardin Mirzapour

In this article, we propose a variational approach for fusion of two coregistered high-resolution panchromatic (HRP) and low-resolution multispectral (LRM) images to reach the high-resolution multispectral (HRM) one, i.e., pan-sharpening. In this fusion technique, there is a tradeoff between structural information of an HRP image and spectral information of LRM one. To reconstruct the HRM image, which benefits from the best characteristics of both images, we consider several fidelity terms. The structural fidelity term is used to transfer structural information of an HRP image to HRM one, and a spectral fidelity term is utilized to preserve spectral consistency between HRM and LRM images throughout the fusion process. To reduce the spectral distortion occurred due to the discrepancy between intensity values of HRP and LRM images, a novel spatial–spectral fidelity term is designed to keep the intensity ratio between multispectral and panchromatic pixels in the high-resolution space as the same as the low-resolution space. Moreover, the total variation (TV) regularization term is employed as a prior to promote the sparseness of gradient in HRM bands. These fidelity terms were formulated in a convex optimization problem. However, the structural and TV terms made this optimization problem nondifferentiable. Therefore, we developed an efficient majorization–minimization algorithm for solving the optimization problem. The proposed method applied to three datasets, acquired by WorldView-3, Deimos-2, and QuickBird satellites. To assess the effectiveness of the proposed method, visual analysis, as well as quantitative comparison to various pan-sharpening methods, was carried out. The experimental results suggested that the proposed method outperformed the competitors visually and quantitatively.

In this study, the authors address the fusion of low‐resolution multi‐spectral image with the corresponding high‐resolution panchromatic image to provide high‐resolution multi‐spectral (HRM) one, i.e. pan sharpening. The intensity–hue–saturation (IHS)‐based pan‐sharpening methods are popular because they are simple, efficient, and of high‐spatial quality. However, their frameworks are unavoidably subject to spectral distortion. To reduce the inevitable spectral distortion of IHS‐based pan‐sharpening approaches, the spectral consistency constraint is used in the proposed method. Moreover, to stabilise fusion results obtained from the ill‐posed pan‐sharpening problem and to keep the smoothness of the HRM image, a total variation regularisation term is considered. These considerations are formulated in a non‐quadratic optimisation problem. To solve this problem, a kind of variable splitting method, known as half‐quadratic approximation is utilised, and also an alternating optimisation procedure is used to reconstruct HRM image. To gain convenient control on the local spectral and the spatial information, and also to reduce the required memory, in the optimisation stage, the patch‐based strategy is employed. The proposed method was tested on two datasets acquired by GeoEye‐1 and Pleiades satellites. To evaluate the proposed method, visual assessment, as well as quantitative comparison with different pan‐sharpening methods, was carried out.

Pan-sharpening (PS) is fusion of the low-resolution multi-spectral (LRM) image with the corresponding high resolution panchromatic (HRP) one, which aims to reach the high-resolution multi-spectral (HRM) image. Due to the importance of designing well-adapted dictionaries for the pansharpening problem as the fundamental challenge in the pansharpening methods utilizing sparse representation, we present a novel strategy to tackle this issue. Our method takes the image formation model into account in a patch-based strategy and exploits local spectral-spatial information. To make a fair tradeoff between spectral and spatial information of each patch, some local parameters are considered and estimated adaptively. In addition, the energy ratio between LRM/LRP patches is considered to locally reconstruct the same ratio for the corresponding HRM/HRP patches. This model-based pansharpening strategy led to a closed-form solution, which results in precise HRM dictionary atoms. To obtain the unknown HRM image, after designing the HRM dictionary from input Pan and multi-spectral images, sparse representation over LRM and HRM dictionaries will be conducted in the sparse coding stage. The proposed method has been applied to two different datasets collected by WorldView-3 and GeoEye-1, and then compared with some popular and state-of-the-art methods, qualitatively and quantitatively.

Persian oak (Quercus Brantii Lindl) is the most abundant tree species in Zagros mountain range that is affected by western dust storms and harsh droughts in recent years. The lack of spectral-temporal information about these trees has caused limitations on the usage of remote sensing images to make a synoptic estimation of damages caused by dust storms and drought. The objective of this research is to analyse the spectral-temporal behaviour of Persian oak under the stress of water deficiency and dust storms. The aim is to improve the competitive abilities of experts on modelling the stress of water deficiency and dust storms on trees using remote sensing images. For the purpose of this experimental investigation, a greenhouse laboratory has been built. Analysis of the experiments was carried out using 54 two-year-old oak tree seedlings; Fieldspec-3-ADD; and wind tunnel at the greenhouse of the faculty of agriculture of the Tarbiat Modares University in 2016. Results show that the water stress could be modelled much better using geometrical indices extracted from continuum removed spectrum. Area and the depth were best indices. Water stress has been modelled better that dust stress.

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. Remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At the present work, we use the moments and Attribute Morphology Profiles (APs) to extract texture information from satellite panchromatic images. We use four conventional moments in pattern recognition such as Geometric, Chebyshev, Legendre and Zernike moments and APs to extract features from remote sensing image. An MP is constructed based on the repeated use of openings and closings by reconstruction of a structuring elements (SE) of an increasing size, applied to a scalar image. Then, we use those two set of features together. The well-known support vector machine (SVM) is used for supervised classification. We compare our proposed method with moments and APs. Different criteria such as average accuracy, overall accuracy, ^κ statistic and computation time are used for assessment of classification performance.

Anomaly detection has been an interesting topic in hyperspectral imagery. Most anomaly detection methods use spectral information for detecting targets. In this paper we propose a method which uses both spectral and spatial information for detecting anomalies: differential morphological profile anomaly detection (DMPAD). This method utilizes principal component analysis (PCA) and differential morphological profile (DMP) to extract spectral and spatial information from hyperspectral image (HSI), respectively. The experimental results confirm DMPAD method's superiority to three mostly used anomaly detection methods, namely PCA, fisher linear discriminant (FLD) and Reed-Xiaoli (RX) methods. DMPAD detects more accurate targets and less false alarm in comparison with competing method.

In this article, we propose a method for extracting spatio-spectral features from high spatial resolution hyperspectral (HS) images. The method is based on extracting two-dimensional moments from neighbourhoods of pixels. Three different types of moments are considered: geometric, complex Zernike and Legendre. Moments of a given type are extracted from a few principal components (PC) of HS data, and are stacked on the original HS data to form a joint spatio-spectral feature space. These features are classified using a support vector machine (SVM) classifier. The influence of the moments orders and the size of the neighbourhood window on the quality of the extracted features are analysed. A few experiments are conducted on two widely used HS data sets, Pavia University and Salinas. The results demonstrate high capabilities of the proposed method in comparison with some state-of-the-art spatio-spectral HS classification methods.