Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

Delalieux, Stephanie; Zarco‐Tejada, Pablo J.; Tits, Laurent; Jiménez-Bello, M.A.; Intrigliolo, Diego S.; Somers, Ben

doi:10.1109/jstars.2014.2330352

Cited by 45 publications

(18 citation statements)

References 82 publications

(1 reference statement)

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“…1) Fusion at subpixel level. The k data sets, which usually involve different spatial scales, are fused at subpixel level using appropriate transforms [26], [27]. 2) Fusion at pixel level.…”

Section: B Standard Fusion Approachesmentioning

confidence: 99%

“…In these cases, the main objective is to preserve the valuable spectral information acquired by multispectral and hyperspectral sensors [33]. Recent proposals based on spectral unmixing have shown good properties providing spectrally consistent fused images and alleviating mixing problems in pixels composed of more than one land cover [27], [45]- [48]. These methods describe a mixed pixel as a combination of pure spectra called endmembers, which contribute into different proportions called fractional abundances [49].…”

Section: B Spatial-spectral Unmixingmentioning

confidence: 99%

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Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Gómez-Chova¹,

et al. 2015

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| Earth observation through remote sensing images allows the accurate characterization and identification of materials on the surface from space and airborne platforms. Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to improve classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sensors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate framework to address these problems. In this paper, we provide a taxonomical view of the field and review the current methodologies for multimodal classification of remote sensing images.We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment. Then, we illustrate the different approaches in seven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image downscaling as a form of multitemporal image fusion and multidimensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image clas, sification; 4) multisensor image fusion exploiting physicallybased feature extractions; 5) multitemporal image classification of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future.

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Section: B Standard Fusion Approachesmentioning

confidence: 99%

Section: B Spatial-spectral Unmixingmentioning

confidence: 99%

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Gómez-Chova¹,

et al. 2015

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“…Despite its attractiveness, fusion in the thermal domain has been studied only scarcely in the IADF community (some exceptions can be found in pansharpening [35], [36] or in the blending of medium (MODIS) and high (Landsat) resolution data [37]), but is attracting increasing interest, e.g., for the fusion of thermal data acquired by unmanned aerial vehicles (UAVs) with airborne hyperspectral images (APEX, for example) [38]- [40] or with digital cameras.…”

mentioning

confidence: 99%

Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest

Liao

Huang

Coillie

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

208

141

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This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.Index Terms-Hyperspectral, image analysis and data fusion (IADF), landcover classification, multimodal-, multiresolution-, multisource-data fusion, thermal imaging.

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“…For example, recent research has demonstrated the potential of hyperspectral data to detect nitrogen stress in potato [40] and water stress in cereals [41] , among numerous other applications. However, for spaceborne instruments, physical limitations result in trade-offs in instrument design, so that hyperspectral data are not generally available at high spatial resolution [42] . Such data acquisition therefore requires ground sensors, costly airborne acquisition or, as an emerging technology, the use of unmanned aerial vehicles.…”

Section: Remote Sensing Of Crop Attributesmentioning

confidence: 99%

A comparative review of the state and advancement of Site-Specific Crop Management in the UK and China

Li¹,

Taylor²,

Frewer³

et al. 2019

Front. Agr. Sci. Eng.

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Precision agriculture, and more specifically Site-Specific Crop Management (SSCM), has been implemented in some form across nearly all agricultural production systems over the past 25 years. Adoption has been greatest in developed agricultural countries. In this review article, the current situation of SSCM adoption and application is investigated from the perspective of a developed (UK) and developing (China) agricultural economy. The current state-of-the art is reviewed with an emphasis on developments in position system technology and satellite-based remote sensing. This is augmented with observations on the differences between the use of SSCM technologies and methodologies in the UK and China and discussion of the opportunities for (and limitations to) increasing SSCM adoption in developing agricultural economies. A particular emphasis is given to the role of socio-demographic factors and the application of responsible research and innovation (RRI) in translating agritechnologies into China and other developing agricultural economies. Several key research and development areas are identified that need to be addressed to facilitate the delivery of SSCM as a holistic service into areas with low precision agriculture (PA) adoption. This has implications for developed as well as developing agricultural economies.

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Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

Cited by 45 publications

References 82 publications

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest

A comparative review of the state and advancement of Site-Specific Crop Management in the UK and China

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