Multimodal Imagery Research Articles

Pulmonary Infundibular Stenosis Secondary to a Mediastinal Hodgkin Lymphoma Tumor Invasion in a Young Adult Male: What a Rare Occurrence!

Introduction: Cardiac involvement is a rare unusual localization of Hodgkin lymphoma, as it’s known that primary lymphomas of the heart are rare. They develop primarily in the right atrium, and extend to the pericardium in about one third of the cases. Pulmonary infundibular stenosis secondary to mediastinal mass Hodgkin lymphoma invasion is rare but not impossible especially in relapse cases. Case Report: A rare case of a young adult patient diagnosed with Hodgkin Lymphoma under chemotherapy presents a relapse of his pathology associated with heart failure; predominantly, right heart failure due to extension of the mediastinal mass tumor to the right cavities of the heart causing pulmonary infundibular stenosis which is a rare occurrence. He was put under an intense chemotherapy and symptomatic heart failure treatment with close follow-up after multi-disciplinary discussions including cardiologist, onco-hematologists and cardiothoracic surgeons. Three months later, a significant reduction of the mass was observered. Conclusion : Pulmonary infundibular stenosis secondary to mediastinal Hodgkin lymphoma invasion is a rare entity of secondary tumors of the heart. Multimodal imagery is the key tool for diagnosis. There prognosis depend on multiple factors but generally good in younger adult patients compared to aged individuals.

Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery

Field-scale crop yield prediction is critical to site-specific field management, which has been facilitated by recent studies fusing unmanned aerial vehicles (UAVs) based multimodal data. However, these studies equivalently stacked multimodal data and underused canopy spatial information. In this study, multimodal imagery fusion (MIF) attention was proposed to dynamically fuse UAV-based RGB, hyperspectral near-infrared (HNIR), and thermal imagery. Based on the MIF attention, a novel model termed MultimodalNet was proposed for field-scale yield prediction of winter wheat. To compare multimodal imagery-based and multimodal features-based methods, a stacking-based ensemble learning model was built using UAV-based canopy spectral, thermal, and texture features. The results showed that the MultimodalNet achieved accurate results at the reproductive stage and performed better than any single modality in the fusion. The MultimodalNet performed best at the flowering stage, with a coefficient of determination of 0.7411 and a mean absolute percentage error of 6.05%. The HNIR and thermal imagery were essential in yield prediction of winter wheat at the reproductive stage. Compared to equivalent stacking fusion, dynamic fusion through adaptively adjusting modality attention improved the model accuracy and adaptability across winter wheat cultivars and water treatments. Equivalently stacking more modalities did not necessarily yield improved performance than dynamically fusing fewer modalities. Methods using multimodal UAV imagery with rich spatial information were more applicable than methods using multimodal features to field-scale yield prediction. This study indicates that the MultimodalNet makes a powerful tool for field-scale yield prediction of winter wheat.

Left atrial dissection after a supra-annular mitral valve replacement for endocarditis

BackgroundLeft atrial dissection is a rare and a potentially fatal complication of cardiac surgery. Multi-modal imagery is helpful for the diagnosis and to guide the treatment.Case presentationWe report the case of a 66-year-old female patient who underwent a combined mitral and aortic valve replacement for degenerative valvular disease. She presented an infectious endocarditis revealed by a third-degree atrioventricular bloc and had a redo mitral- and aortic valve replacement. Mitral valve was inserted in supra-annular position due to annular destruction. Post-operative course was marked by a refractory acute heart failure explained by a left atrial wall dissection confirmed by transoesophageal echocardiography and synchronized cardiac CT-scan. Surgical treatment was theoretically indicated but considering the high risk of a third surgery, a palliative care support was collegially decided.ConclusionsLeft atrial dissection can occur after a redo surgery and supra-annular mitral valve implantation. Multi-modal imagery including transoesophageal echocardiography and cardiac CT-scan is helpful for the diagnosis.

MBAHIL: Design of a Multimodal Hybrid Bioinspired Model for Augmentation of Hyperspectral Imagery via Iterative Learning for Continuous Efficiency Enhancements

The augmentation of hyperspectral images requires the design of high-density feature analysis & band-fusion models that can generate multimodal imagery from limited information sets. The feature analysis models use deep learning operations to maximize inter-class variance while minimizing inter-class variance levels for efficient classification operations. When combined with intelligent band-fusion methods, such models allow the augmentation model to enhance its classification efficiency under different use cases. Existing band-fusion-based augmentation models for hyperspectral images do not incorporate continuous efficiency enhancements and showcase higher complexity levels. Furthermore, these models can’t be scaled for more varied use cases because their use is restricted to specific image types. To overcome these issues, we designed a novel multimodal hybrid bioinspired model for the augmentation of hyperspectral imagery via iterative learning for continuous efficiency enhancements. The proposed model initially represents input images into Fourier, Laplacian, Cosine, multimodal Wavelet, Mellin, and Z-Transform domains, which will assist in describing the images in multimodal domains. These transformed image sets are passed through a convolutional filter to extract windowed feature sets. A Grey Wolf Optimizer (GWO) is used to identify high inter-class variance features from the extracted image sets, which assists in selecting transformed images that can help improve hyperspectral classification performance. The selected hyperspectral images are fused via a Bacterial Foraging Optimization (BFO) model, which assists in reducing intra-class variance levels. The final set of selected images is intelligently augmented via Particle Swarm Optimization (PSO), which performs rotation, zooming, shifting, and brightness variation operations selectively. These augmented images are classified via a customized VGGNet-19-based Convolutional Neural Network (CNN) classifier that assists in continuously estimating accuracy levels for different application scenarios. Based on these accuracy levels, the model is reconfigured via hyperparameter tuning of GWO and PSO optimizers. Due to combining these models and incremental accuracy optimizations, the proposed model has improved its hyperspectral classification accuracy by 10.6% and precision by 10.4%, as compared to standard deep learning-based augmentation techniques.

Multimodal imaging using tellurite highly nonlinear multimode fibers

Since the development of the first fibroscope in the late nineteenth century, the use of innovating glass fibers has opened promising perspectives for medical applications particularly in the domain of multimodal imagery[1]. In this work, tellurite glasses have been used to design step-index optical fibers with a rectangular core-section for supercontinuum generation in the near infrared domain. The tellurite glasses selection and the fibers manufacturing by the stack-and-draw process will be presented. Characterizations performed on bulk samples and fibers will also be detailed[2]. The spectral broadening, which is obtained in this work in a short fiber sample, is used for imaging mouse kidney cells, labelled with three different fluorochromes, by means of two and three-photon absorption. Despite the multimode nature of the output beam, clear images of tubules, actin and nucleus are collected with a spatial resolution down to 1.2 µm[3].

Multimodal imagery in picture books for children

This research focuses on revealing visual means of creating imagery in picture books, addressed to a child-reader. The theory of J. Appleyard has been acknowledged. It is believed that in early and later childhood children realize themselves as players, heroes, and heroines of the stories. Given that, visual imagery contributes to their reading experience and creates special world, which attracts child-readers. The theory suggested by G. Kress and T, van Leeuwen, supplemented by C. Painter, J. Martin, and L. Unsworth has been developed and amplified. Social distance, proximity, attitude, and contact in picture books for children are created by means of visual devices, i.e. color and saturation, page layout, close up and long-shot depiction. It is assumed that the most prominent means of creating meanings in picture books for children becomes the focaliser’s viewpoint, as in most narratives for children a child or an anthropomorphic character of a child’s age is a focaliser of the events. It is claimed that close-up depiction of images creates a special eye contact with the reader, as well as color decisions become visual means of creating the point of view of the focaliser. Furthermore, full-length pictures become a visual tool of implementation ironic senses. In ironic narrative for children, the incongruity is shaped via visual depiction of the characters, which builds the contrast between conventional understanding of issues and their representation in narratives for children. Proximity and distance of the images implements social and psychological connection, status or misunderstanding between the characters. Special angle of viewing the images serves as a device of revealing the type of the narrator of the story, as oblique and high angle become a tool of narrating a story by an adult narrator.

Trauma, Multimodal Mental Imagery and Intermediality in Arundhati Roy’s The God of Small Things

Arundhati Roy’s The God of Small Things has been effectively described by N. Rokotnitz as a narrative that accesses and arouses sensory modes of reception and perception, infiltrating reader’s preconscious and subconscious levels of understanding and thus enhancing the vividness of immersion in a text that ultimately renders its moral code physically tangible. This use of multimodal mental imagery, which per se vouches for a transmedia understanding of narrative as a “multimedia construct” (Ryan 2004), is an integral part of a literary representation of trauma that also integrates explicit reference to other media, namely cinema and traditional dance performance. The essay addresses the role of these media within the socio-cultural frame of Roy’s novel and interrogates their function in shaping the literary narrative of trauma and healing, integrating the writing’s multimodal imagery and contributing to the reader’s emotional and intellectual responses.

Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe

Generating tree-specific crop maps within heterogeneous landscapes requires imagery of fine spatial and temporal resolutions to discriminate among the rapid transitions in tree phenological and spectral features. The availability of freely accessible satellite data of relatively high spatial and temporal resolutions offers an unprecedented opportunity for wide-area land use and land cover (LULC) mapping, including tree crop (e.g., mango; Mangifera indica L.) detection. We evaluated the utility of combining Sentinel-1 (S1) and Sentinel-2 (S2) derived variables (n = 81) for mapping mango orchard occurrence in Zimbabwe using machine learning classifiers, i.e., support vector machine and random forest. Field data were collected on mango orchards and other LULC classes. Fewer variables were selected from ‘All’ combined S1 and S2 variables using three commonly utilized variable selection methods, i.e., relief filter, guided regularized random forest, and variance inflation factor. Several classification experiments (n = 8) were conducted using 60% of field datasets and combinations of ‘All’ and fewer selected variables and were compared using the remaining 40% of the field dataset and the area underclass approach. The results showed that a combination of random forest and relief filter selected variables outperformed (F1 score > 70%) all other variable combination experiments. Notwithstanding, the differences among the mapping results were not significant (p ≤ 0.05). Specifically, the mapping accuracy of the mango orchards was more than 80% for each of the eight classification experiments. Results revealed that mango orchards occupied approximately 18% of the spatial extent of the study area. The S1 variables were constantly selected compared with the S2-derived variables across the three variable selection approaches used in this study. It is concluded that the use of multi-modal satellite imagery and robust machine learning classifiers can accurately detect mango orchards and other LULC classes in semi-arid environments. The results can be used for guiding and upscaling biological control options for managing mango insect pests such as the devastating invasive fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae).

Deep-Pest-Detector: Automated Detection and Localization of Processionary Moth Nests on Pine Trees via Aerial Drones and Deep Neural Networks

HighlightsA deep learning architecture, which relies on multi-modal imagery from the RGB and thermal spectra, is proposed to detect PPM nests via aerial drones.A GPS-based computation method is implemented to geo-tag the position of the detected nests in the drone’s perimeter.A Kalman filter is utilized as a nest tracker to avoid reporting back the position of the same nest several times.The system is tested in a pine forest and exhibits accurate detection and localization, as evidenced by reporting back the positions of the detected PPM nests.Abstract.The pine processionary moth (PPM) is considered the main defoliator of pine trees and is a menacing threat to various other perennial species including oak and cedar. Given their negative secondary effects, spraying of pesticides has been banned as a means for the eradication of PPM; instead, an individualized approach is to be adopted, in which each nest is localized and destroyed. Detection of PPM nests using optical sensing is challenging because of the changing outdoor lighting conditions and the camouflaged appearance of moth in the underlying foliage. This article proposes a promising solution for nest detection by fusing sensory data from an RGB camera on one hand, and a thermal camera on the other, both mounted on an aerial drone. The proposed detection system is built on a two-channeled deep convolutional neural network, one for each spectrum of the collected sensory data. Experiments performed in a pine forest demonstrate successful detection rates with an average accuracy of 97% in various experiments and settings. Geo-localization is performed to report the position of the detected nests within a scanned forest map by means of an estimation scheme that is particularly designed for this purpose achieving centimeter accuracy (<20 cm). Keywords: Aerial robotics, Agriculture, Detection, Machine learning, Multi-stream CNN, Pest control.

The Role of Multimodal Imagery in Life Writing

Life writers describe extraordinary experiences that often extend far beyond the everyday lives of the readers they are trying to reach. How memoirists try to bring their pasts alive in readers’ minds goes to the heart of why they write. Moving readers emotionally requires close engagement that can often be achieved through sensory simulation. As psychologists such as Lawrence Barsalou and literary scholars such as G. Gabrielle Starr have shown, fiction-writers and poets involve their readers by encouraging them to recreate their own past bodily sensations, especially multi-modal sensations (such as vision and touch) that blend as in lived experience. In this study of Jean-Dominique Bauby’s The Diving Bell and the Butterfly and Natasha Trethewey’s Memorial Drive, I will examine how two gifted memoirists encourage readers to imagine in several sensory modalities at once. Through expert use of language, Bauby and Trethewey open their experiences to a wide range of readers to share the most acute human joys and sufferings.

Multi-sensor anomalous change detection in remote sensing imagery

Combining multiple satellite remote sensing sources provides a far richer, more frequent view of the earth than that of any single source; the challenge is in distilling these petabytes of heterogeneous sensor imagery into meaningful characterizations of the imaged areas. Meeting this challenge requires effective algorithms for combining multi-modal imagery over time to identify subtle but real changes among the intrinsic data variation. Here, we implement a joint-distribution framework for multi-sensor anomalous change detection (MSACD) that can effectively account for these differences in modality, and does not require any signal resampling of the pixel measurements. This flexibility enables the use of satellite imagery from different sensor platforms and modalities. We use multi-year construction of the SoFi Stadium in California as our testbed, and exploit synthetic aperture radar imagery from Sentinel-1 and multispectral imagery from both Sentinel-2 and Landsat 8. We show results for MSACD using real imagery with implanted, measurable changes, as well as real imagery with real, observable changes, including scaling our analysis over multiple years.

The functions of heraldic symbols in the English fiction

The article considers the main functions performed by heraldic symbols due to their verbalization in blazons as descriptions of coats of arms by a specific set of language constructs. Heraldic symbols as the quintessence of culture are analyzed in the communicative-functional aspect. There are some special characteristics of heraldic symbolic units, namely, their multifunctional orientation, the possibility of multiple interpretations, the presence of a nomination mechanism, and others. The mediative and mnemonic functions of symbols, taken from the works of Lotman, are manifested in the heraldic symbols of fiction as one of the mechanisms of cultural memory, therefore, the symbol is a mediator of the semiosis spheres. The list of functions is supplemented by the consideration of the social dimension, because heraldic symbols are potentiated in the society, therefore, the essence of the identification-integrative and adaptive-regulatory functions of heraldic symbolism is revealed. The concepts of multimodal imagery and synesthesia effect, which are embodied in the interaction of verbal and nonverbal symbols, are involved in the analysis of the blazons on the material of English fiction. The theme of symbols functioning is supplemented by the classification of the main functions and subfunctions proposed by the author. The focus of the study is also the identification function, resulting in an emphasis on the role of a special heraldic language in the expression of the personality. Taking into account the types of coats of arms, the article considers the coding function in the punning and allusive coats of arms, as well as in the heraldic lexemes borrowed from other languages. The cross-cutting idea of the article is revealed through the interpretation of heraldic symbols in their functioning in the works of English fiction and poetry and summarized in the definition of the symbol not only as a linguistic unit, but also as a semiotic phenomenon of culture.

The Language of Fear in Children’s Literature: A Case Study of Ted Hughes’s Poems for Children

The article tackles the issue of the language of fear exploited in children’s literature, taking Ted Hughes’s Nature poems for young readers as the object of analysis. It presents a perspective of linguistic stylistics and literary semantics and as such is not meant to be a critical literary evaluation of Hughes’s poetry. Rather, it focuses on linguistic instruments of creating the aura of fear in children’s poetry and their cognitive import. The author has chosen a neuroscientific paradigm for the two closely related emotions – fear and anxiety – as propagated by American researcher Joseph LeDoux, most prominently in his work “Anxious” (2015). LeDoux maintains that the feeling of fear is not inborn but rather a cognitive construct emergent from the use of one’s native language practiced within a particular socio-cultural context. The unique atmosphere of Hughes’s poetry has been achieved by a rich lexicon of fear-related notions and a skillfully applied figuration (anthropomorphisms, similes). His poetic imagery powerfully complements the vocabulary and troping in calling to life fictional worlds, often uncanny and menacing, remote from the young readers’ experience. The author of this article perceives in the lexicon, figuration and multimodal imagery (both verbal and visual, the latter realized as illustrations in picture-books) an important didactic device that teaches children how to manage fearsome experiences. This capability will also prepare children to face anxiety, an emotion typical of adult life and related mostly to existential problems.

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity.

Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert–Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.

Expertise Shapes Multimodal Imagery for Wine.

Although taste and smell seem hard to imagine, some people nevertheless report vivid imagery in these sensory modalities. We investigate whether experts are better able to imagine smells and tastes because they have learned the ability, or whether they are better imaginers in the first place, and so become experts. To test this, we first compared a group of wine experts to yoked novices using a battery of questionnaires. We show for the first time that experts report greater vividness of wine imagery, with no difference in vividness across sensory modalities. In contrast, novices had more vivid color imagery than taste or odor imagery for wines. Experts and novices did not differ on other vividness of imagery measures, suggesting a domain‐specific effect of expertise. Critically, in a second study, we followed a group of students commencing a wine course and a group of matched control participants. Students and controls did not differ before the course, but after the wine course students reported more vivid wine imagery. We provide evidence that expertise improves imagery, exemplifying the extent of plasticity of cognition underlying the chemical senses.

Unsupervised classification of land cover using multi-modal data from multi-spectral and hybrid-polarimetric SAR imageries

ABSTRACT Current research investigations using remotely sensed images are offered with a plethora of sources to explore land cover/land use applicability. Some of the recent advances have shown the advantage of fusing different data sources in land-cover analysis. Though intuitively combined processing of multi-modal imagery should provide better classification of land cover, there are not many work towards this direction and a theoretical framework is not laid out properly. In this work, we are providing such a framework where scattering and spectral properties (from synthetic aperture radar and multi-spectral images, respectively) of ground materials are used to distinguish land-cover classes with higher precision. Different kinds of information that are represented by these two modes of imageries are semantically bridged to infer more distinguishable land-cover classes in an unsupervised framework. The proposed technique is implemented in two phases, i.e., (1) sampling of seed pixels from imageries, and (2) training of representative features and prediction of classes using random forest classifier. Experimental results also show the effectiveness of this fusion of multi-modal image characteristics in classifying the underlying land cover.

SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization.

PURPOSEWe present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors.PATIENTS AND METHODSIn this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI.RESULTSWe illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each.CONCLUSIONSlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

JOINT GEOMETRIC CALIBRATION OF COLOR AND THERMAL CAMERAS FOR SYNCHRONIZED MULTIMODAL DATASET CREATING

Abstract. With increasing performance and availability of thermal cameras the number of applications using them in various purposes grows noticeable. Nowadays thermal vision is widely used in industrial control and monitoring, thermal mapping of industrial areas, surveillance and robotics which output huge amount of thermal images. This circumstance creates the necessary basis for applying deep learning which demonstrates the state-of-the-art performance for the most complicated computer vision tasks. Using different modalities for scene analysis allows to outperform results of mono-modal processing, but in case of machine learning it requires synchronized annotated multimodal dataset. The prerequisite condition for such dataset creating is geometric calibration of sensors used for image acquisition. So the purpose of the performed study was to develop a technique for joint calibration of color and long wave infra-red cameras which are to be used for collecting multimodal dataset needed for the tasks of computer vision algorithms developing and evaluating.The paper presents the techniques for camera parameters estimation and experimental evaluation of interior orientation of color and long wave infra-red cameras for further exploiting in datasets collecting. Also the results of geometrically calibrated camera exploiting for 3D reconstruction and 3D model realistic texturing based on visible and thermal imagery are presented. They proved the effectivity of the developed techniques for collecting and augmenting synchronized multimodal imagery dataset for convolutional neural networks model training and evaluating.

SWT and PCA image fusion methods for multi-modal imagery

Image fusion is the process of combining two or more related images to produce a single output image, containing more relevant information than any one of the input images. The image-fusion process depends upon: the application domain; the number of images undergoing fusion; and the type of imagery, such as whether it is multi-spectral or multi-modal. For clarity of presentation, this paper takes two important fusion methods, Stationary Wavelet Transform (SWT) and Principal Components Analysis (PCA), and applies them to a variety of imagery. Results show that in multi-modal image fusion, PCA appears to perform better for those input images that have different contrast/brightness levels. SWT appears to give better performance when the input images are multi-modal and multi-sensor. A feature of the paper are the number of objective functions employed to evaluate the SWT and PCA methods, allowing the utility of each to be judged. The reader will also find in this paper a concise guide to image fusion techniques with clear recommendations on how to evaluate them.

Interactive multi-image colour visualization for hyperspectral imagery

ABSTRACTData visualization can accelerate data processing so that enormous quantities of data can be utilized effectively. Visualization of data can achieve image communication between people and data as well as between people to help observers get information hidden in data, providing a tool for discovery and understanding of scientific law. To solve the problem of multi-image and multi-modality image display in the field of remote sensing, an interactive colour visualization method for hyperspectral imagery (HSI) is proposed in this article. This method visualizes complex information of original HSI data through different fusion results of multiple images in a colour space, which is under the interactive control of the observers. By gradually determining predetermined points, observers can obtain a relatively satisfying image blending mode, output an image with clearer interested target, and obtain the corresponding mixing coefficient of images. The proposed method can also solve the problem that traditional visualization methods only display information from three bands in one image, and conduct information mining in HSI with a certain purpose based on the demands of users. In addition, this approach is also applicable for visualization of other types of multi-modal imagery.