Auxiliary Image Research Articles

ПОСТРОЕНИЕ ЦИФРОВОЙ МОДЕЛИ РЕЛЬЕФА МЕТОДОМ РАДИОЛОКАЦИОННОЙ ИНТЕРФЕРОМЕТРИИ

The article discusses the experience of applying the technique of constructing a digital elevation model (DEM) by the method of radar interferometry using Sentinel-1A / 1B data on the territory of the Amur region near the city of Svobodny in June 2019. The presented method, which consists of 9 main stages, is implemented in the PHOTOMOD Radar software package. The stages of processing include: loading the initial data and converting them into the internal format of the program; creating a project for processing and combining images of an interferometric pair (recalculating the raster of the auxiliary image into the geometry of the main image); construction pictures of interferogram and coherence and it is also possible to calculate pictures of phase and amplitude; filtering the interferogram and phase transformation (recalculation of the obtained values in meters); construction of a digital elevation model and its geocoding into a geographic coordinate system (WGS 84). After that, the results obtained are assessed and compared with the SRTM digital elevation model, namely, a visual comparison of two digital elevation models, their subtraction (SRTM – model obtained from Sentinel-1A / 1B data) and a comparison heights characteristic relief points. Also made conclusions and considered the possibilities of further projects

Massive-Scale Genre Communities Learning Using a Noise-Tolerant Deep Architecture

Accurately categorizing million-scale Internet users (e.g., Flickr or Google Picasa) into multiple communities based on their genre tastes is an indispensable techniquein machine learning and multimedia. It can facilitate a series of applications, such as fashion recommendation and 3D non-realistic photo rendering. Conventional methods cannot handle this task appropriately because of the inherent contaminated image labels, which are produced by auxiliary image label predictors. In this article, we propose a noise-tolerant deep architecture which optimally encodes stable templates, 1 1 Stable template is our proposed new concept. It denotes the distribution of co-occurring semantic categories toward an image set. Both theoretical and empirical analysises have demonstrated that such distribution (i.e. stable templates) remains almost unchanged in the presence of contaminated image tags. discovered from a collection of images with contaminated semantic labels. Specifically, we first construct a semantic space by encoding image labels using manifold embedding. Afterward, we observe that in the semantic space, the distribution of superpixels from images with the same label remains stable, regardless of the noises from image labels. According to this observation, a probabilistic generative model (Hidden Stable Analysis) is proposed to learn the stable templates toward each image label. To globally represent the composition of a user's images, a deep aggregation network is developed which statistically concatenates the CNN features learned from all its generated stable templates. Subsequently, an affinity graph is built, in which the genre difference among users is determined by their deep features. Finally, we employ a dense subgraph discovery technique which effectively mines the communities toward various genre tastes. Experiments on a million-scale image set ( $>$ 1.4 million) compiled from Flickr have demonstrated the effectiveness of our method. Additionally, empirical study on the 33 SIFT-flow categories have shown that the detected stable templates maintain almost unchanged under nearly 32% contaminated image labels.

MAPPING SYSTEM AND PHOTOGRAMMETRIC PROCESSING METHOD FOR TETHERED BALLOON PLATFORM

Abstract. As a platform with the advantages of safety, wide altitude range and long flight time, tethered balloon has been used in archaeology, coastal, island mapping and other related fields. Tethered balloon is tied to the mooring system by tether rope, and often moves in a small range with the airflow, resulting in a very close distance between the camera stations. Due to the small base-to-height ratio, failure of relative orientation or poor quality of point cloud tend to appear frequently. In this paper, a mapping system for tethered balloon is introduced which consists of a wide-angle camera, two auxiliary orientation cameras, and a dual GNSS/IMU system. The wide-angle camera combined by five cameras provides a larger observation range. Two auxiliary orientation cameras installed at the end of extension rods are designed to increase the baseline. The system should be operated during the taking-off, hovering and landing stages of the tethered balloon, so as to take images of different altitudes. The system has been adopted in the first experimental flight in 2018 to perform island topographic survey. Data processing method was applied to test the effect of the system. The method that choosing wide-angle images of different heights, and auxiliary images at low-altitude can be used for aerial triangulation successfully, and obtain point cloud with good quality. The results showed that by adding auxiliary cameras and taking images of different heights for data processing, the problem of small intersection angle could be overcome to some extent.

Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction

Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.

Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition.

The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained image recognition. Unfortunately, user click frequency data are usually absent in practice. It remains challenging to predict the click feature from the visual feature, because the user click frequency vector of an image is always noisy and sparse. In this paper, we devise a Hierarchical Deep Word Embedding (HDWE) model by integrating sparse constraints and an improved RELU operator to address click feature prediction from visual features. HDWE is a coarse-to-fine click feature predictor that is learned with the help of an auxiliary image dataset containing click information. It can therefore discover the hierarchy of word semantics. We evaluate HDWE on three dog and one bird image datasets, in which Clickture-Dog and Clickture-Bird are utilized as auxiliary datasets to provide click data, respectively. Our empirical studies show that HDWE has 1) higher recognition accuracy, 2) a larger compression ratio, and 3) good one-shot learning ability and scalability to unseen categories.

A Multi-Scale Wavelet 3D-CNN for Hyperspectral Image Super-Resolution

Super-resolution (SR) is significant for hyperspectral image (HSI) applications. In single-frame HSI SR, how to reconstruct detailed image structures in high resolution (HR) HSI is challenging since there is no auxiliary image (e.g., HR multispectral image) providing structural information. Wavelet could capture image structures in different orientations, and emphasis on predicting high-frequency wavelet sub-bands is helpful for recovering the detailed structures in HSI SR. In this study, we propose a multi-scale wavelet 3D convolutional neural network (MW-3D-CNN) for HSI SR, which predicts the wavelet coefficients of HR HSI rather than directly reconstructing the HR HSI. To exploit the correlation in the spectral and spatial domains, the MW-3D-CNN is built with 3D convolutional layers. An embedding subnet and a predicting subnet constitute the MW-3D-CNN, the embedding subnet extracts deep spatial-spectral features from the low resolution (LR) HSI and represents the LR HSI as a set of feature cubes. The feature cubes are then fed to the predicting subnet. There are multiple output branches in the predicting subnet, each of which corresponds to one wavelet sub-band and predicts the wavelet coefficients of HR HSI. The HR HSI can be obtained by applying inverse wavelet transform to the predicted wavelet coefficients. In the training stage, we propose to train the MW-3D-CNN with L1 norm loss, which is more suitable than the conventional L2 norm loss for penalizing the errors in different wavelet sub-bands. Experiments on both simulated and real spaceborne HSI demonstrate that the proposed algorithm is competitive with other state-of-the-art HSI SR methods.

Life and Variety：The toad and the Soul View in Xi Wang Mu image in the Han stone engraving

<p>西王母的崇拜與信仰自漢代流傳至今而不絕，影響深遠，二十世紀以來，因漢畫像的出土發現，西王母圖像大量出現在漢代墓室祠堂中，這些考古材料又重新活化漢代西王母的研究。今考漢畫像西王母圖像系統，並非以單一主神出現，其空間語境多配以不同的輔助圖像。本文研究主要以漢畫像西王母仙境圖為探討範圍，以漢畫像西王母仙境圖中「必要圖像」蟾蜍為研究對象，並旁及「月中蟾」的討論﹔以見漢畫像中西王母與蟾蜍的配置關係，及漢代西王母的神格功能與象徵。歷來研究者多由「不死藥」連結蟾蜍與月亮、乃至於西王母的關係；而西王母在漢代墓室中其執掌又與靈魂的轉化再生有關，對漢畫像西王母仙境圖中蟾蜍圖像之考察，不死，是共同的焦點。此與古代漢人生死觀、魂魄觀有密切的關係，值得進一步梳理探討。</p> <p> </p><p>The worship and belief of the Queen Mother of the West（Xi Wang Mu） have been circulated and influenced since the Han Dynasty. Since the 20th century, due to the excavation of the portraits of the Han Dynasty, the images of the Xi Wang Mu appeared in the portraits of the tombs of the Han Dynasty. These archaeological materials undoubtedly reactivated the study of the Queen Mother of the West（Xi Wang Mu）of the Han Dynasty. In the Han Dynasty portrait stone, the image does not appear as a single main god, and its spatial context is often accompanied by different auxiliary images. This thesis mainly focuses on the West Queen Motherland of the Han Dynasty, and takes the "necessary image" the toad in the West Queen Motherland as the research object, and next to the discussion of " the toad in the moon ", to see the relationship between The Queen Mother of the West（Xi Wang Mu） and the toad in Han stone engraving, and The function and symbol of the goddess of The Queen Mother of the West. Traditional scholars have always been associated with the moon and even the Western Queen by "immortal medicine"; but the Western Queen Mother（Xi Wang Mu）is the chain of souls in the tomb of the Han Dynasty. It is a common focus on the image of the tomb in the Han Tomb. This is closely related to the ancient Han people’s outlook on life and death, and it is worth further exploration to sort out the relationship.</p> <p> </p>

Human Emotion Recognition System using Machine Learning for Videos

Emotion is a key element in user-generated video. However, it is difficult to understand emotions conveyed in such videos due to the complex and unstructured nature of user-generated content and the sparsity of video frames expressing emotion. In this paper, for the first time, we propose a technique for transferring knowledge from heterogeneous external sources, including image and textual data, to facilitate tasks in understanding video emotion recognition. After that the audio files are played based on the emotion in the video frames. Specifically, our framework (1) learns a video encoding from an auxiliary emotional image dataset in order to improve supervised video emotion recognition. A comprehensive set of experiments on multiple datasets demonstrate the effectiveness of our framework. At first the video is separated into number of frames. Then the separated frames are taken for the analyzing using Convolutional Neural Network (CNN). The process is used to analysis the video frames to identify the emotion that describe in the videos. After that the audio files are played based on the emotion in the video frames. As a result, after processing the frames through CNN layers it provides a sound clip according to each emotions.

A Spatiotemporal Fusion Based Cloud Removal Method for Remote Sensing Images With Land Cover Changes

Cloud contamination greatly limits the potential utilization of optical remote sensing images for geoscience applications. Many solutions have been developed to remove the clouds from multispectral images. Among these approaches, the temporal-based methods which borrow complementary information from multitemporal images outperform the other methods. However, the common fundamental supposition of the temporal-based methods decides that they are only suitable for scenes with phenological changes, while they perform poorly in cases with significant land cover changes. In this paper, a cloud removal procedure based on multisource data fusion is developed to overcome this limitation. On the basis of the temporal-based approaches, which employ a cloud-free image as reference, this method further introduces two auxiliary images with similar wavelengths and close acquisition dates to the reference and target (contaminated) images into the reconstruction process. The temporal variability of the land cover is captured from the two auxiliary images through a modified spatiotemporal data fusion model, and thus, the serious errors produced by the temporal-based methods can be avoided. Moreover, a residual correction strategy based on the Poisson equation is used to enhance the spectral coherence between the recovered and remaining regions. The experiments confirmed that the proposed method can perform very well for cases with significant land cover changes. Compared with some state-of-the-art approaches, it produces lower bias and more robust efficacy. In conclusion, our method will act as an important technical supplement to the current cloud removal framework, and it provides the possibility to handle scenes with significant land cover changes.

Seepage mechanism technical practice of hydraulic fracturing of coal seam and auxiliary image simulation technology

A new method of fracturing hydraulic fracturing is proposed for the characteristics of poor coal permeability and soft coal in coal mine area in China. This method can make the hydraulic pressure concentrated on the coal at one point, and the water pressure is reduced, Effective water pressure increases, the smaller the flow that can get a better fracture effect. Through the numerical simulation, the law of fracture distribution and permeability evolution during segmental hydraulic fracturing is analyzed. Complete sets of anti-reflection equipment for segmented hydraulic fracturing have been developed, which is mainly composed of mobile high-pressure pump station, high-pressure rubber hose, water injector, putters and corresponding connecting parts. It solves the problem of tightness of hydraulic fracturing device, and breaks through the problem of miniaturization of high pressure pumping station, so that the high pressure pumping station can achieve larger working pressure at smaller flow rate. The whole process is simulated and validated by assistant image simulation technology, and applied to Pansidong Coal Mine of Huainan Mining Group. The industrial test of hydraulic directional fracturing of Pan coal mine in Huainan mining group shows that the loose influence range of coal can reach 10 m, and the pressure of underground movable high pressure hydraulic pumping station reaches 25 MPa, and the flow rate is 180 L/min. Compared with non fracturing drilling, drilling, the average gas concentration after fracturing, the average daily average flow rate of mixed gas drainage quantity is improved significantly, the application effect is good, and the equipment is simple, easy to use, can be repeatedly used, and has better economic benefit and application prospect.

Incorporating structural prior information and sparsity into EIT using parallel level sets

EIT is a non-linear ill-posed inverse problem which requires sophisticated regularisation techniques to achieve good results. In this paper we consider the use of structural information in the form of edge directions coming from an auxiliary image of the same object being reconstructed. In order to allow for cases where the auxiliary image does not provide complete information we consider in addition a sparsity regularization for the edges appearing in the EIT image. The combination of these approaches is conveniently described through the parallel level sets approach. We present an overview of previous methods for structural regularisation and then provide a variational setting for our approach and explain the numerical implementation. We present results on simulations and experimental data for different cases with accurate and inaccurate prior information. The results demonstrate that the structural prior information improves the reconstruction accuracy, even in cases when there is reasonable uncertainty in the prior about the location of the edges or only partial edge information is available.

Improving image similarity estimation via global distance distribution information

Estimating the similarity between two images or image patches is at the heart of many computer vision problems including content-based image retrieval, image registration, and scene recognition. However, commonly used distance-based similarity estimation is not always reliable due to the limitations in both image understanding techniques and distance metrics. In this paper, we present a scheme to improve the similarity estimation under image search scenario. To this end, we explore the discriminative capability underlying global distance distribution obtained by querying an auxiliary image dataset in an unsupervised manner. According to the results of motivational experiments, we discover that global distance distributions have the desired capability in distinguishing inter-class images which can be applied to enhance the original distance metric. Following this finding, we propose a novel approach to incorporate the global distance distribution into the original distance metric to improve the reliability of the similarity estimation. One key novelty of this approach is to model the global distance distribution as Rayleigh distribution and then represent the difference between two distributions by the relative entropy. In this way, the difference between two global distance distributions can be calculated in an extremely efficient way. We also demonstrate that Rayleigh distribution leads to consistent performance compared to the real distribution. Extensive experiments on three public datasets with various image representations and distance metrics show that the enhanced similarity estimation remarkably outperforms the original one. Furthermore, the proposed approach shows the desired scalability for handling large-scale image search scenarios.

Identificação de possíveis áreas salinizadas no perímetro irrigado de São Gonçalo utilizando o sensoriamento remoto

Os perímetros irrigados implantados no Estado da Paraíba são considerados uma alternativa econômica bastante rentável, promove a geração de empregos e aumenta a disponibilidade de alimentos. Devido ao manejo inadequado do solo e da água, isso têm causado perdas na qualidade do solo desses perímetros, degradando-os principalmente por salinização. O sensoriamento remoto é uma alternativa tecnológica de baixo custo, boa frequência temporal e possui a capacidade de mapear áreas em processo de desertificação. Essa pesquisa têm por objetivo identificar possíveis áreas afetadas por sais no Perímetro Irrigado de São Gonçalo (PISG), Sousa/PB, por meio de técnicas de sensoriamento remoto. Para esse estudo foi utilizado imagens do satélite LANDSAT 8/OLI (média resolução espacial), órbita 216/ponto 65 da data de 23/11/2016; imagem do software Google Earth Pro® da data de 29/02/2016 para servir como imagem auxiliar e registros fotográficos das áreas in loco. Realizou-se a técnica de classificação supervisionada, utilizando o SCP (semi- automatic plugin) no software QGIS (Quantum Gis). A aferição da qualidade da classificação se deu por meio da validação cruzada, utilizando de parâmetros estatísticos como a exatidão do produtor (EP), exatidão do usuário (EU), exatidão global (EG) e índice Kappa. A classe área supostamente salinizada (ASS) apresentou EP e EU de 89.15% e 88.88%, respectivamente. O índice Kappa resultou em um valor de 0.8684, a classe ASS foi classificada como sendo de qualidade excelente. A qualidade geral da classificação é avaliada tanto pela EG que apresentou um valor de 0.9350 como pelo índice Kappa geral com valor de 0.9252, sendo valores que representam uma classificação de qualidade excelente. A classe ASS apresentou os maiores valores mínimos e máximos de fator de refletância em todas as bandas da imagem, destacando a banda 6 de valores 0.47 e 0.67, respectivamente. O valor da área classificada como sendo da classe ASS foi de 1736.75 hectares, 31% da área total do PISG. As imagens analisadas possibilitaram discriminar áreas salinizadas e não salinizadas mediante as diferenças de tonalidade e de refletância. As imagens analisadas com o plugin SCP possibilitaram a realização de um mapa de classificação supervisionada, indicando a variabilidade espacial das áreas propícias ao processo de salinização. No entanto, recomenda-se a análise dos parâmetros físicos e químicos do solo dessas áreas para o aumento da confiabilidade na qualidade desse tipo de mapeamento.
 
 
 
 
 
 
 
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 Africâner
 Albanês
 Alemão
 Arabe
 Armênio
 Azerbaijano
 Basco
 Bengali
 Bielo-russo
 Birmanês
 Bósnio
 Búlgaro
 Catalão
 Cazaque
 Cebuano
 Chichewa
 Chinês (Simp)
 Chinês (Trad)
 Cingalês
 Coreano
 Crioulo haitiano
 Croata
 Dinamarquês
 Eslovaco
 Esloveno
 Espanhol
 Esperanto
 Estoniano
 Finlandês
 Francês
 Galego
 Galês
 Georgiano
 Grego
 Gujarati
 Hauça
 Hebraico
 Hindi
 Hmong
 Holandês
 Húngaro
 Igbo
 Indonésio
 Inglês
 Ioruba
 Irlandês
 Islandês
 Italiano
 Japonês
 Javanês
 Kannada
 Khmer
 Laosiano
 Latim
 Letão
 Lituano
 Macedônico
 Malaiala
 Malaio
 Malgaxe
 Maltês
 Maori
 Marathi
 Mongol
 Nepalês
 Norueguês
 Persa
 Polonês
 Português
 Punjabi
 Romeno
 Russo
 Sérvio
 Sesotho
 Somália
 Suaíli
 Sudanês
 Sueco
 Tadjique
 Tagalo
 Tailandês
 Tâmil
 Tcheco
 Telugo
 Turco
 Ucraniano
 Urdu
 Uzbeque
 Vietnamita
 Yiddish
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Cloud removal for hyperspectral remotely sensed images based on hyperspectral information fusion

ABSTRACTHyperspectral remote sensing plays an important role in a wide variety of fields. However, its specific application for land surface analysis has been constrained due to the different shapes of thick, opaque cloud cover. The reconstruction of missing information obscured by clouds in remote-sensing images is an area of active research. However, most of the available cloud-removal methods are not suitable for hyperspectral images, because they lose the spectral information which is very important for hyperspectral analysis. In this article, we developed a new spectral resolution enhancement method for cloud removal (SREM-CR) from hyperspectral images, with the help of an auxiliary cloud-free multispectral image acquired at different times. In the fixed hyperspectral image, spectra of the cloud cover pixels are reconstructed depending on the relationship between the original hyperspectral and multispectral images. The final resulting image has the same spectral resolution as the original hyperspectral image but without clouds. This approach was tested on two experiments, in which the results were compared by visual interpretation and statistical indices. Our method demonstrated good performance.

A Robust Fuzzy C-Means Algorithm Based on Bayesian Nonlocal Spatial Information for SAR Image Segmentation

The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM $\_$ BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM $\_$ BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.

Asymmetry and symmetry of valley-river of landscape and technical systems

The aim of the article is to consider the manifestation of landscape asymmetry and symmetry within the valley-river landscape and technical systems, which are confined to the flat part of Right-Bank Ukraine, on the basis of a generalized spatial and temporal analysis. Methods. The study is based on the ideas of F. M. Milkov on anthropogenic landscapes and «Pierre Curie principles»: the universality of symmetry, the forced symmetry and dynamism of symmetry, which were adapted in physical geography. Scientific novelty. In practice, application of the principles of symmetry and asymmetry has been widely implemented in landscape design and architecture in the design and construction of gardening landscape. In modern Ukrainian landscape science, this issue is only beginning to be considered at both theoretical, and practical levels. Individual publications are devoted to this problem, but there is no detailed research where asymmetry and symmetry of valley-river landscape and technical systems would be analyzed. Practical value is determined by the introduction into the practice of working with landscape and technical systems of universal principles of symmetry and asymmetry, which will make it possible to find effective ways of rational use of natural resources. Research results. It was noted that in the study of landscape and technical systems in them it is worth to distinguish elements of symmetry and asymmetry – auxiliary geometric images (points, lines, planes) in respect of which equal parts of geographical objects are repeated in a certain way. In complex landscape and technical systems basic engineering structures often play the role of such geometric images. It is noted that the leading role in the formation of the asymmetry of valley-river landscapes is played by the anthropogenic factor. Under its influence, natural landscape asymmetry can radically change. Depending on the development stages of landscape and technical systems, possible variants of landscape asymmetry and symmetry transformation from natural to anthropogenic are analyzed. On the example of valley-river landscape and technical systems of Right-Bank Ukraine (reservoirs, ponds, canals and bridges), manifestations of anthropogenic landscape asymmetry and symmetry are considered. Based on our own field research and spatial and temporal analysis of the economic development of river valleys of Right-Bank Ukraine, a model for the development of a typical pond valley-river landscape and technical system in the Southern Bug basin was developed, where one can observe the process of landscape asymmetry complication in various respects. The most typical variant of transformation is the one in which the natural landscape asymmetry changes into anthropogenic and manifests itself at all stages of development. It is concluded that the violation of geometric images of valley-river landscape and technical systems occurs as a result of the ineffective activity of the control unit. Consideration of individual properties of asymmetry and symmetry of valley-river landscape and technical systems is necessary for solving many problems associated with environmental management and the optimization of such systems. To this end, it is necessary to increase the attention of modern geographers and landscape scholars to study the problems of the influence of asymmetry and symmetry on the structure, dynamics and stability of landscape and technical systems.

A Hybrid Subpixel Mapping Framework for Hyperspectral Images Using Collaborative Representation

Subpixel mapping with a low-resolution hyperspectral image as the only input is widely applicable due to the fact that auxiliary image is not always available in practice. In this paper, the collaborative representation-based subpixel mapping (CRSPM) framework is proposed to acquire an improved classification map at subpixel scale with only a low-resolution hyperspectral image available. To efficiently extract and utilize spatial information in this case without auxiliary image, the low-resolution hyperspectral (LHS) image is processed in a hybrid framework in two different ways to generate two subpixel scale classification maps. One is obtained by classifying the upsampled LHS image using collaborative representation-based (CR-based) classifier. The other is available using CR-based classification combined with spectral unmixing and subpixel spatial attraction model. Specifically, to enclose the contextual spatial information for higher classification accuracy, a spatially joint as well as post-partitioning CR-based classifier, JCRT-based classifier, is proposed and applied in this work. To achieve better classification performance, decision fusion is applied to determine class label from the two classification maps for each subpixel by the voting of the neighboring subpixels. Experimental results illustrate that the proposed CRSPM approach clearly outperforms some state-of-the-art subpixel mapping approaches by producing smoother classification map with less misclassification.

Usar la palabra para volver a narrar: la relación arte-literatura en la obra de Jaume Plensa

La cuestión de la autonomía del arte respecto a la literatura ha supuesto un continuo debate en el mundo del arte. En este artículo tratamos la relación entre arte y literatura y cómo Jaume Plensa plantea una nueva ilación donde la obra de arte no pretende ser mera imagen auxiliar del texto que la inspira. En este contexto, hemos analizado las obras del escultor catalán en relación a las fuentes literarias de que las parten, tratando de desvelar los distintos caminos de representación escultórica que estos relatos han forjado dentro del imaginario de Plensa.

Hyperspectral Image Super-Resolution by Spectral Difference Learning and Spatial Error Correction

A hyperspectral image (HSI) super-resolution (SR) is a highly attractive topic in computer vision. However, most existed methods require an auxiliary high-resolution (HR) image with respect to the input low-resolution (LR) HSI. This limits the practicability of these HSI SR methods. Moreover, these methods often destroy the important spectral information. This letter presents a deep spectral difference convolutional neural network (SDCNN) with the combination of a spatial-error-correction (SEC) model for HSI SR. This method allows for full exploration of the spectral and spatial correlations, which achieves a good spatial information enhancement and spectral information preservation. In the proposed method, the key band is automatically selected and super-resolved with the boundary bands. Meanwhile, spectral difference mapping between the LR and HR HSIs can be learned by the SDCNN, and then be transformed according to the SEC model, which aims at correcting the spatial error while preserving the spectral information. The rest nonkey bands will be super-resolved under the guidance of the transformed spectral difference. Experimental results on synthesized and real-scenario HSIs suggest that the proposed method: 1) achieves comparable performance without requiring any auxiliary images of the same scene and 2) requires less computation time than the state-of-the-art methods.

Auxiliary pixel data selection for recovering Landsat ETM+ SLC-off images

A number of gap-filling algorithms have been developed since Landsat 7 ETM+ scan line corrector (SLC) permanent failure. However, little research has been done to select the reliable auxiliary pixel data from the extra auxiliary images. The goal of our study is to develop a pixel level auxiliary data selection method to choose the appropriate pixel data automatically from different sources to fill the un-scanned pixels of SLC-off image. Based on adaptive window linear histogram match (AWLHM), a pixel level auxiliary data selection algorithm was established through the similarity indicator. A case study was carried out to restore a representative SLC-off image around Hangzhou Bay, East China. It indicates that the pixel level auxiliary data selection algorithm is an effective tool to choose the reliable pixel data from the available sources for filling un-scanned pixels, and the recovery similarity distribution map can be obtained for the filled image, which can be used to conduct both the subsequent improvement and the reliability estimation.