Assessment of pan-sharpened very high-resolution WorldView-2 images

The fusion of multispectral (MS) images with high spatial resolution panchromatic (pan) images compensates the trade-off between spatial and spectral resolutions. The performance of fusion algorithms for different sensors is an active area of research. Therefore, with the availability of new very high-resolution (VHR) sensors, it becomes customary to evaluate the applicability of existing fusion techniques. In this study, we focused on the WorldView-2 (WV2) spaceborne sensor, which captures data in eight MS (2 m spatial resolution) bands and one pan (0.5 m spatial resolution) band. We compared and assessed 12 fusion techniques, namely Brovey transform (BT), Ehlers, Gram–Schimdt (GS), hyperspherical colour sphere (HCS), high-pass filter (HPF), modified intensity hue saturation (ModIHS), Multiplicative, PANSHARP, PANSHARP2, principal component (PC), and wavelet (IHS and PC)-based methods. To measure the quality of fused products, qualitative and quantitative methods were used. In qualitative methods, visual analysis of different colour composites was carried out. In quantitative methods, band-wise eight quality metrics including the required processing time and controlling parameters were reported. For overall image quality assessment, it is necessary to combine the values of different quality metrics. Therefore, a mean observation score based on these values was calculated to rank the fusion methods. It was observed that the HPF and PANSHARP methods produced the most visually appealing images, whereas quantitative values indicated that HCS, HPF, and PANSHARP methods performed better than other methods. Combining the results of quantitative and qualitative analyses, it was found that PANSHARP and HPF methods are superior to other methods in preserving both spatial and spectral details.     

Spatial–spectral ant colony optimization for hyperspectral image classification

Hyperspectral satellite images contain a lot of information in terms of spectral behaviour of objects and this information can be extracted by several mechanisms including image classification. Traditional spectral information-based methods of hyperspectral image classification are generally followed by spatial information-driven post-processing techniques such as relaxation labelling and Markov Random Field. Spectral or spatial information alone may lead to different results depending upon scene captured. An algorithm which can incorporate influence of both spectral and spatial features is needed to address this problem. In this article, an ant colony optimisation-based hyperspectral image classification technique is proposed. This method exploits both spatial and spectral features. Five standard hyperspectral data sets have been used to validate the proposed method and comparisons with other approaches have been carried out. It was observed that the proposed method yielded a significant improvement in classification accuracy. For the instance, nearly 10% increase in accuracy was observed when compared to Support Vector Machine for Indian pines, Botswana, and Salinas images.     

Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details

Image fusion techniques are widely used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image, such as Thematic Mapper (TM) multispectral band and SPOT Panchromatic images. However, the existing techniques either cannot avoid distorting the image spectral properties or involve complicated and time-consuming frequency decomposition and re-construction processing. A simple spectral preserve fusion technique: the Smoothing Filter-based Intensity Modulation (SFIM) has thus been developed based on a simplified solar radiation and land surface reflection model. By using a ratio between a higher resolution image and its low pass filtered (with a smoothing filter) image, spatial details can be modulated to a co-registered lower resolution multispectral image without altering its spectral properties and contrast. The technique can be applied to improve spatial resolution for either colour composites or individual bands. The fidelity to spectral property and the spatial textural quality of SFIM are convincingly demonstrated by an image fusion experiment using TM and SPOT Panchromatic images of south-east Spain. The visual evaluation and statistical analysis compared with HSI and Brovey transform techniques confirmed that SFIM is a superior fusion technique for improving spatial detail of multispectral images with their spectral properties reliably preserved.     

Single subject image analysis using the complex general linear model--an application to functional magnetic resonance imaging with multiple inputs

A linear time invariant model is applied to functional fMRI blood flow data. Based on traditional time series analysis, this model assumes that the fMRI stochastic output sequence can be determined by a constant plus a linear filter (hemodynamic response function) of several fixed deterministic inputs and an error term assumed stationary with zero mean. The input function consists of multiple exponential distributed (time delay between images) visual stimuli consisting of negative and erotic images. No a priori assumptions are made about the hemodynamic response function that, in essence, is calculated at each spatial position from the data. The sampling rate for the experiment is 400 ms in order to allow for filtering out higher frequencies associated with the cardiac rate. Since the statistical analysis is carried out in the Fourier domain, temporal correlation problems associated with inference in the time domain are avoided. This formal model easily lends itself to further development based on previously developed statistical techniques.     

A wavelet-based classification of hyperspectral images using Schroedinger eigenmaps

This article proposes a new algorithm for hyperspectral image classification. The proposed method is a spectral–spatial method based on wavelet transforms, kernel minimum noise fraction (KMNF) and spatial–spectral Schroedinger eigenmaps (SSSE). To overcome the computation complexity, one-dimensional discrete wavelet transform (1D-DWT) is applied in spectral domain. To reduce noise, KMNF coefficients are extracted in wavelet space. To solve time-consuming problem, 2D-DWT coefficients are employed in spatial space. Hence, the combination of 1D-DWT, KMNF, and 2D-DWT is suggested to create SSSE features. The classification is carried out by a Support Vector Machine (SVM) classifier. Experimental results show that classification accuracy and time consumption are effectively improved compared to the state-of-the art reported spectral–spatial SVM-based methods.     

A comparative assessment of different methods for Landsat 7/ETM+ pansharpening

This article compares a set of relevant methods, based on different mathematical approaches, for Landsat 7 Enhanced Thematic Mapper Plus (ETM+) pansharpening. These are classical procedures such as principal component analysis and fast intensity hue saturation; methods based on wavelet transforms, such as wavelet à trous, additive wavelet luminance proportional and multidirectional–multiresolution methods; a method of a geostatistical nature, called downscaling cokriging (DCK); and finally, a Bayesian method (1cor). The comparison of the fused images is based on the qualitative and quantitative evaluation of their spatial and spectral characteristics by calculating statistical indices and parameters that measure the quality and coherence of the images. Moreover, the quality of the spectral information is studied indirectly by means of the Iterative Self-Organizing Data Analysis Technique (ISODATA) classification of the products of fusion. The results show that DCK and 1cor methods yielded better results than the wavelet-based methods. Particularly, DCK does not introduce artefacts in the estimation of the digital numbers corresponding with the source multispectral image and, therefore, it can be considered as the most coherent method.     

Border-tracing algorithm implementation for the femoral geometry reconstruction

In some orthopaedic applications such as the design of custom-made hip prostheses, reconstruction of the bone morphology is a fundamental step. Different methods are available to extract the geometry of the femoral medullary canal from computed tomography (CT) images. In this research, an automatic procedure (border-tracing method) for the extraction of bone contours was implemented and validated. A composite replica of the human femur was scanned and the CT images processed using three different methods, a manual procedure; the border-tracing algorithm; and a threshold-based method. The resulting contours were used to estimate the accuracy of the implemented procedure. The two software techniques were more accurate than the manual procedure. Then, these two procedures were applied to an in vivo CT data set in order to determine to most critical region for repeatability. Only for the images located in this region, the repeatability measurement was carried out for six in vivo CT data sets to evaluate the inter-femur repeatability. The border-tracing method was found to achieve the highest repeatability.     

Combining the spectral PCA and spatial PCA fusion methods by an optimal filter

High correlation among the neighboring pixels, both spectrally and spatially in a multispectral image makes it indispensable to use relevant data transformation approaches, before performing image fusion. The principal component analysis (PCA) method has been a popular choice for the spectral transformation. To propose a new consistent data transformation method in spatial domain, this paper applies the PCA transform to the spatial information of the neighboring pixels. Owing to the fact that the coefficients of PCA are obtained from statistical properties of data, they are adaptive and robust. Then, a new hybrid algorithm is proposed combining the spectral PCA and spatial PCA methods, by an optimal filter to make the synthesized result more similar to what the corresponding multisensors would observe at the high-resolution level. The evaluation of the pan-sharpened images, using global validation indexes, reveals that the proposed approach improves the fusion quality compared with six state of the art fusion methods.     

图像方向性在空域和频域中的特性研究

文章研究了图像的方向性在空域和频域分析中的不同特性。首先,建立了图像的空域模型和频域模型,对两者的特性进行了数学分析,并且通过一定的仿真实验进行了验证。结果表明,图像的空域方向性和频域方向性之间存在着确定的关联性。这一特性在图像的频域分析中有着至关重要的作用。     

Spatio-spectral fusion of satellite images based on dictionary-pair learning

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches.     

Integration of Landsat and aeromagnetic data as aid to the structural analysis of Crete and S.E. Peloponessus

Abstract

The paper comments on the usefulness of remotely-sensed data (Land-sat MSS images in both digital and photographic format—aeromagnetic data) in the tectonic analysis of areas of Greece. The island of Crete was the main case study area, while a general analysis was also carried out in the South Eastern Peloponessus.

Both image processing (spectral and spatial analysis) of the Landsat CCTs of Crete and computer analysis of the features mapped on the images have been carried out. Aeromagnetic data are also analysed using advanced processing techniques (spectral analysis and deconvolution). Structural interpretations are improved by the study of the enhanced Landsat images and aeromagnetic maps, while the use of the various computer techniques makes the analysis of the mapped patterns easier and more accurate.

The combined interpretation of aeromagnetic and Landsat MSS data added several significant structural features, previously unrecognised from separate interpretations of aeromagnetic data and Landsat images.     

Imaging cerebral function

The Scan Analysis and Visualization Processor (Scan/VP), a flexible, portable, Unix-based software package for visualizing and analyzing positron emission tomography (PET) images in a clinical-research setting, is described. PET systems are compared to computerized tomography (CT) and magnetic resonance imaging (MRI) systems. The imaging and software aspects of Scan/VP, and procedures devoted specifically to functional PET imaging, including mathematical modeling, image registration, regional thresholding, and derivation of regional covariation patterns, are discussed. Basic surface display, animation, and stereo techniques for visualizing variations in metabolic topology and underlying disease patterns are also discussed     

Spatial–spectral preprocessing for spectral unmixing

Most techniques available in the endmember extraction rely on exploiting the spectral information of the data alone. In this paper, we improve the utilization of data information by dividing a pixel into four subpixels which are redefined by the scalar factor related to the spatial–spectral similarity. The spatial information is integrated into the spectral information in a certain spatial neighbourhood domain, which can make extracted endmembers more precisely, because the effect of noise and outliers can be suppressed with preprocessing (PP). Meanwhile, the accuracy of spectral unmixing will be improved without modification to the conventional methods applied to spectral-based endmember extraction. Experimental results with both synthetic and real hyperspectral images demonstrate the unmixing accuracy is better than that without PP.     

Information extraction from very high resolution satellite imagery over Lukole refugee camp,Tanzania

This paper addresses information extraction from IKONOS imagery over the Lukole refugee camp in Tanzania. More specific, it describes automatic image analysis procedures for a rapid and reliable identification of refugee tents as well as their spatial extent. From the identified tents, the number of refugees can be derived and a map of the camp can be generated, which can be used for improving refugee camp management. Four information extraction methods have been tested and compared: supervised classification, unsupervised classification, multi-resolution segmentation and mathematical morphology analysis. The latter two procedures based on object-oriented classifiers perform best with a spatial accuracy above 85% and a statistical accuracy above 97%. These methods could be used for refugee camp information extraction in other geographical settings and on imagery with different spatial and spectral resolutions.     

两种高保真遥感影像融合方法比较

遥感影像融合有着广泛的应用前景。定量遥感不仅要求影像融合提高空间分辨率，更重要的是保持影像光谱信息，减少失真。为了使人们对不同遥感影像融合方法优缺点有一概略了解，首先详细介绍了两种新的高保真融合算法(基于亮度调节的平滑滤波和Gram-Schmidt变换)的原理和方法；然后以城区IKONOS影像为数据源，通过目视判别、定量统计参数和图形法3种方法对两种融合算法进行了比较，并与传统的融合算法IHS变换和PC变换进行了对比。结果表明，4种融合算法的空间效果是相似的，但从对光谱信息的保真来看，PC变换和IHS变换都较差，基于亮度调节的平滑滤波保真效果最好，Gram-Schmidt变换次之，但Gram-Schmidt变换保真效果已比PC变换和IHS变换有了较大的提高。     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.     

An automatic nonlinear correlation approach for processing of hyperspectral images

Hyperspectral imaging technology demands sophisticated processing techniques that offer precise characterizations of complex spectral signatures. A nonlinear correlator structure is implemented for interference mitigation and object recognition. A key asset is the correlator's applicability to both the spatial (two-dimensional) and spectral (one-dimensional) domains, thus ideal for hyperspectral processing. The process consists of a standard convolution summed with a nonlinear adaptive term. The premise is the same in each case but the mathematical implementation is different. By performing the correlation calculations in the frequency domain, the processing algorithm is efficient, robust, and well suited for implementation on a parallel processing computational architecture. The nonlinear correlator depends on two parameters and an algorithm to determine these parameters based only on the input image (two-dimensional) or spectral signature (one-dimensional) is presented. Based on the results with the selected spatial and spectral templates, a target is identified and the spatial coordinates as well as the spectral signature are input to the final fusion stage, which analyses both spectral and spatial signatures for a correct target identification. Several examples are given and insights to template (mask) selection are provided.     

Multitemporal SAR images for monitoring cultivation systems using case-based reasoning

This paper demonstrates that multitemporal satellite SAR images are most suitable for monitoring the rapid changes of cultivation systems in a subtropical region. A new method is proposed by applying case-based reasoning (CBR) techniques to the classification of SAR images. Stratified sampling is carried out to collect the cases so that the variations of backscatters within a class can be appropriately captured. The use of discrete cases can conveniently represent the internal changes of a class under complicated situations, such as spatial changes in soil conditions and terrain features. These spatial variations are difficult to represent by using rules or mathematical equations. The proposed method has better classification performance than supervised classification methods in the study area. The case library is reusable for time-independent classification when the SAR images are acquired at the same time of the crop growth cycles for different years. The proposed method has been tested in the Pearl River Delta in South China.     

Comparison of different pan-sharpening methods for spectral characteristic preservation: multi-temporal CARTOSAT-1 and IRS-P6 LISS-IV imagery

A host of remote-sensing and mapping applications require both high spatial and high spectral resolutions. Availability of high spatial and spectral details at different resolutions from a suite of satellite sensors has necessitated the development of effective image fusion techniques that can effectively combine the information available from different sensors and take advantage of their varied capabilities. A common problem observed in the case of multi-sensor multi-temporal data fusion is spectral distortion of the fused images. Performance of a technique also varies with variation in scene characteristics. In this article, two sets of multi-temporal CARTOSAT-1 and Indian Remote Sensing satellite (IRS-P6) Linear Imaging and Self Scanning sensor (LISS-IV) image sub-scenes, with different urban landscape characteristics, are fused with an aim to evaluate the performance of five image fusion algorithms – high-pass filtering (HPF), Gram–Schmidt (GS), Ehlers, PANSHARP and colour-normalized Brovey (CN-Brovey). The resultant fused data sets are compared qualitatively and quantitatively with respect to spectral fidelity. Spatial enhancement is assessed visually. The difference in the performance of techniques with variation in scene characteristics is also examined. For both scenes, GS, HPF and PANSHARP fusion techniques produced comparable results with high spectral quality and spatial enhancement. For these three methods, the variation in performance over different scenes was not very significant. The Ehlers method resulted in spatially degraded images with a more or less constant negative offset in data values in all bands of one scene and in the first two bands in the other. The CN-Brovey method produced excellent spatial enhancement but highly distorted radiometry for both sub-scenes.     

A semi-blind HVS based image watermarking scheme using elliptic curve cryptography

In the present paper, an advanced encryption technique commonly known as Elliptic Curve Cryptography (ECC) is used to embed a binary image as a watermark in five grayscale host images in a semi-blind manner. The ECC algorithm is a fast encryption technique which successfully encrypts the subject with significantly less number of bits as compared to other popular encryption algorithms such as Rivest-Shamir-Adleman (RSA) and Direct Selling Association (DSA). In the proposed watermarking scheme, embedding in the grayscale host images is carried out in DWT-SVD domain. First, entropy based Human Visual System (HVS) parameters are computed block wise to identify the most appropriate blocks in spatial domain. First level DWT is computed for these selected blocks and watermark embedding is carried out by using the calculated Singular Value Decomposition (SVD) parameters. Preliminary results of this work show that proposed scheme outperforms the other similar schemes carried out in DCT-SVD domain without using any encryption method. It is concluded that the use of DWT-SVD hybrid architecture along with the fast encryption technique ECC is responsible for better performance in present case. In the second part of this simulation, an established HVS model working in DCT domain is implemented and compared with the entropy based HVS model implemented in transform domain to embed the ECC encrypted binary watermark in images. In this case also, proposed scheme performs better both in terms of visual imperceptibility and robustness as compared to other scheme. It is concluded that HVS parameters – Luminance, Contrast and Edge Sensitivity are better placed in comparison to entropy parameters to examine image features and characteristics for watermarking purpose.