Analysis of Multiple Scattering of Radiation amongst End Members in a Mixed Pixel of Hyperspectral Data for Identification of Mangrove Species in a Mixed Stand

Chakravortty, Somdatta; Sinha, Devadatta

doi:10.1007/s12524-014-0437-x

Cited by 7 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most studies on mangrove species classification were conducted using pixel-based methods such as spectral angle mapper (SAM) [45,46], maximum likelihood classification (MLC) [7,8,46], and spectral unmixing [47][48][49], or object-based methods, such as nearest neighbor (NN) [20,21], random forest (RF) [50], and support vector machine (SVM) [14,51,52]. Previous studies have shown that the object-based methods generally outperformed the pixel-based methods for mangrove species classification, particularly with high-resolution hyperspectral images [21,[53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

View full text Add to dashboard Cite

Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi'ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

show abstract

Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In linear spectral unmixing, assuming y as the intensity value of each pixel in the hyperion image and m r as the matrix representing the signature of pure mangrove species identified by NFINDR, the fractional abundance a r is estimated using the equation However, in mixed natural forests like the Sunderban nonlinear models that consider radiation interaction between several objects within a pixel area and manifold scattering between the plant canopies are likely to provide precise end-member detection of species and their abundance estimation. In bilinear models, it is implicit that the radiation incident on each mangrove end-member suffers reflections with other mangrove species existing in close vicinity 31 . The radiation reflected from the initial end-member is reflected once more by the subsequent end-member and then intercepted by the satellite sensor.…”

Section: Nonlinear Modelmentioning

confidence: 99%

Development of Higher-Order Model for Nonlinear Interactions in Hyperspectral Data of Mangrove Forests

Chakravortty¹,

Sinha²

2016

Current Science

Self Cite

View full text Add to dashboard Cite

The present article analyses the accuracy of application of higher-order nonlinear interaction models on hyperspectral data to identify mangrove mixtures present in the Sunderbans Delta -a World Heritage Site in West Bengal, India. It is observed that intra-species interaction between similar mangrove species (interaction between the same type of end-members) in a homogeneous mangrove stand is more accurately modelled by the linear-quadratic model and hence results in more accurate fractional abundance estimations after unmixing when compared with linear-unmixing models. Specifically, we observe that quadratic models provide more accurate estimates than linear and bilinear models for the study area (Henry Island of Sunderbans), which is mostly dominated by pure and mixed mangrove species of Avicennia marina, Excoecaria agallocha, Avicennia alba, Phoenix paludosa, Avicennia officinalis, Ceriops decandra, Bruguiera cylindrica and Aegialitis. In this study, the quadratic nonlinear model successfully characterizes the interaction of endmember mixtures comprising E. agallocha, A. officinalis, B. cylindrica and A. alba in the study area.Keywords: Higher-order interaction models, hyperspectral data, mangrove species, nonlinear interactions.HYPERSPECTRAL remote sensing (HSRS) is a powerful tool for detailed spatio-temporal mapping and sustainable management of large forested lands. The wide spectral range of hyperspectral data and their high spectral resolution allows for accurate detection and classification of surface canopies and ground features through the application of hyperspectral image processing algorithms 1,2 . For example, the characteristic bio-optical properties of different mangrove species in a dense mangrove forest can be integrated into spectral libraries for improved discrimination and mapping of mangrove eco-types.Outside India, researchers have made a detailed study of mangroves at species level using hyperspectral data 3-5 . Studies in India have also established the capability of hyperspectral data for species-level discrimination of mangroves in distantly located islands of the Sunderban Delta. The Delta is well-known for its homogeneous and heterogeneous patches of mangrove species that include a particular form or various forms of mangrove species within the ecosystem. Application of remote sensing technologies for accurate identification and discrimination of mangrove species is being considerably encouraged in recent times. Homogeneous mangrove areas could be identified through linear spectral unmixing, which follows the principle of singular reflection with negligible multiple scattering 6 . However, in natural forests such as the Sunderban Biosphere Reserve, intra-and inter-species scattering occurs, thus making the reflections nonlinear in nature 7 . Nonlinear spectral unmixing is expected to provide a more accurate estimate of fractional abundance estimation of mangrove mixtures along with their identification.Nonlinear models have been developed at microscopic scale for materials which a...

show abstract

“…Extensive research works have been done in the literature to address spectrum unmixing problems based on linear and nonlinear approaches. Nonlinear methods can simulate physical phenomena well, so they can bring better separation or unmixing performance for some applications [11]. Besides, nonlinear methods are usually associated with complex mathematical representations and are applicable to limited applications [12].…”

Section: Introductionmentioning

confidence: 99%

Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures

Panchal¹,

Shivaputra²

2022

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Hyperspectral image (HSI) has been widely adopted in many real-world applications due to its potential to provide detailed information from spectral and spatial data in each pixel. However, precise classification of an object from HSI is challenging due to complex and highly correlated features that exhibit a nonlinear relationship between the acquired spectral unique to the HSI object. In literature, many research works have been conducted to address this problem. However, the problem of processing high-dimensional data and achieving the best resolution factor for any set of regions remains to be evolved with a suitable strategy. Therefore, the proposed study introduces simplified modeling of the hyperspectral image in which precise detection of regions is carried out based on the characterization of pure signatures based on the estimation of the maximum pixel mixing ratio. Moreover, the proposed system emphasizes the pixel unmixing problem, where input data is processed concerning wavelength computation, feature extraction, and hypercube construction. Further, a non-iterative matrix-based operation with a linear square method is performed to classify the region from the input hyperspectral image. The simulation outcome exhibits efficient and precise object classification is achieved by the proposed system in terms classified HSI object and processing time.</span>

show abstract

Analysis of Multiple Scattering of Radiation amongst End Members in a Mixed Pixel of Hyperspectral Data for Identification of Mangrove Species in a Mixed Stand

Cited by 7 publications

References 25 publications

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Development of Higher-Order Model for Nonlinear Interactions in Hyperspectral Data of Mangrove Forests

Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures

Contact Info

Product

Resources

About