Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation

Veganzones, Miguel Ángel; Tochon, Guillaume; Mura, Mauro Dalla; Plaza, Antonio; Chanussot, Jocelyn

doi:10.1109/tip.2014.2329767

Cited by 84 publications

(44 citation statements)

References 43 publications

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“…Segmentation Over-Segmentation Figure 1. Coarse-scale decomposition of a section of the Cuprite image for bands 50, 80 and 100 using the segmentation algorithm in [14] and the oversegmentation algorithm in [15], with 950 and 2000 segments, respectively.…”

Section: Hs Imagementioning

confidence: 99%

A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

Borsoi

Imbiriba

Bermudez

et al. 2019

IEEE Geosci. Remote Sensing Lett.

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Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can greatly benefit from spatial regularization strategies. However, existing spatial regularization strategies lead to large-scale nonsmooth optimization problems. Thus, efficiently introducing spatial context in the unmixing problem remains a challenge, and a necessity for many real world applications. In this paper, a novel multiscale spatial regularization approach for sparse unmixing is proposed. The method uses a signal-adaptive spatial multiscale decomposition based on segmentation and over-segmentation algorithms to decompose the unmixing problem into two simpler problems, one in an approximation image domain and another in the original domain. Simulation results using both synthetic and real data indicate that the proposed method outperforms stateof-the-art Total Variation-based algorithms with a computation time comparable to that of their unregularized counterparts.

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Section: Hs Imagementioning

confidence: 99%

A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

Borsoi

Imbiriba

Bermudez

et al. 2019

IEEE Geosci. Remote Sensing Lett.

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“…In this type of data, each pixel of the image is a complete radiance or reflectance spectrum, augmenting considerably the spectral information contained in a pixel, with respect to RGB or multispectral images, for instance. This spectral information enables different types of applications [1], such as segmentation [2], classification [3], target detection [4]... However, the acquisition of data in many wavelengths comes with a lower spatial resolution than color or even multispectral images.…”

mentioning

confidence: 99%

Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability

Drumetz

Veganzones

Henrot

et al. 2016

IEEE Trans. on Image Process.

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197

204

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Spectral unmixing is one of the main research topics in hyperspectral imaging. It can be formulated as a source separation problem, whose goal is to recover the spectral signatures of the materials present in the observed scene (called endmembers) as well as their relative proportions (called fractional abundances), and this for every pixel in the image. A linear mixture model (LMM) is often used for its simplicity and ease of use, but it implicitly assumes that a single spectrum can be completely representative of a material. However, in many scenarios, this assumption does not hold, since many factors, such as illumination conditions and intrinsic variability of the endmembers, induce modifications on the spectral signatures of the materials. In this paper, we propose an algorithm to unmix hyperspectral data using a recently proposed extended LMM. The proposed approach allows a pixelwise spatially coherent local variation of the endmembers, leading to scaled versions of reference endmembers. We also show that the classic nonnegative least squares, as well as other approaches to tackle spectral variability can be interpreted in the framework of this model. The results of the proposed algorithm on two different synthetic datasets, including one simulating the effect of topography on the measured reflectance through physical modelling, and on two real data sets, show that the proposed technique outperforms other methods aimed at addressing spectral variability, and can provide an accurate estimation of endmember variability along the scene because of the scaling factors estimation.

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“…However, the topographic representation is typically built on the gray-level image, which rarely concerns color difference. In [26][27][28][29][30], various types of images, e.g., natural images, hyperspectral images, and PolSAR images, were represented by a hierarchical structure, namely, Binary Partition Tree (BPT), which was constructed based on particular region models and merge criteria. BPT can represent multi-scale objects from fine to coarse levels.…”

Section: Related Workmentioning

confidence: 99%

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

Yang

Xia

et al. 2016

Remote Sensing

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Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT), which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

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Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation

Cited by 84 publications

References 43 publications

A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

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