High spatial resolution spectral unmixing for mapping ash species across a complex urban environment

Liu

IEEE Trans. Geosci. Remote Sensing

2020

187

Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high spectral correlation, which would lead to the so-called Hughes phenomenon and the high computational cost in processing. Band selection has been proven effective in avoiding such problems by removing the redundant bands. However, many of existing band selection methods separately estimate the significance for every single band and cannot fully consider the nonlinear and global interaction between spectral bands. In this paper, by assuming that a complete HSI can be reconstructed from its few informative bands, we propose a general band selection framework, Band Selection Network (termed as BS-Net). The framework consists of a band attention module (BAM), which aims to explicitly model the nonlinear inter-dependencies between spectral bands, and a reconstruction network (RecNet), which is used to restore the original HSI cube from the learned informative bands, resulting in a flexible architecture. The resulting framework is end-to-end trainable, making it easier to train from scratch and to combine with existing networks. We implement two BS-Nets respectively using fully connected networks (BS-Net-FC) and convolutional neural networks (BS-Net-Conv), and compare the results with many existing band selection approaches for three real hyperspectral images, demonstrating that the proposed BS-Nets can accurately select informative band subset with less redundancy and achieve significantly better classification performance with an acceptable time cost.

Section: Branchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image

Liu

IEEE Trans. Geosci. Remote Sensing

2020

187

“…The majority of studies concerning vegetation health condition were performed using aerial (including hyperspectral) and satellite imagery. A number of authors (Holopainen et al 2006, Hanou 2010, Zhang et al 2014, Pontius et al 2017 compared the health condition of healthy, diseased and dead trees with vegetation indices, or determined relationships between the variables obtained from RS data and biophysical variables (e.g., defoliation and discoloration). Malthus & Younger (2000) determined the correlations between an overall tree condition index (OTC), defoliation, and selected vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), the Green Nor-malized Difference Vegetation Index (gNDVI), and the red edge position (REP).…”

Section: Health Conditionmentioning

confidence: 99%

Accuracy of determining specific parameters of the urban forest using remote sensing

Ciesielski¹,

Stereńczak²

2019

iForest

This paper reviews the current state of knowledge in the field of urban forest inventory and specific tree parameters derived by remote sensing. The paper discusses the possibilities and limitations of using remote sensing to determine the following characteristics of individual trees acquired during the inventory: position (coordinates), tree height, breast height diameter, tree crown parameters (crown span, height of tree crown basis, crown projection surface), health condition, and tree species. A total of 543 papers published in scientific databases (Scopus ® and ScienceDirect ® ) from the year 2000 to December 2017 have been analyzed; 86 of them were used for the review. The most important outcomes are: (a) the integration of many datasets, in particular spectral data (aerial images and satellite imageries) and structural data (LIDAR), allows the most complex use of remote sensing data and helps to improve the accuracy of parameter estimations as well as the correct identification of tree species; (b) the highest precision of measurement is characteristic of TLS, while ALS data has the largest operating system; (c) remote sensing data applications are associated with a large number of sophisticated processing on very large datasets using often proprietary elaborations; (d) the use of remote sensing data makes it possible to determine the characteristics of urban vegetation at various levels of detail and at different scales. Citation: Ciesielski M, Sterenczak K (2019). Accuracy of determining specific parameters of the urban forest using remote sensing. iForest 12:

“…The combination of leaf-on and leafoff data from the same year, on the other hand, can help quantifying deciduous tree cover or surface cover underneath canopies. The advantage of multiple images per year is even higher when mapping tree species: information from various dates to better describe phenology stages will increase separability and improve characterization of vegetation structure and vigor (e.g., using multispectral data refer to Pontius et al 2017;Tigges et al 2013;Wirion et al 2017). Denser time series of acquisitions may help characterize phenology and the influences of, for example, neighboring artificial surfaces on microclimate models.…”

Section: User and Observational Requirementsmentioning

confidence: 99%

“…We define the term urban environment in this paper as contiguous areas of anthropogenic, artificial surfaces used for transport, commerce, production, administration or housing, plus the included or adjacent vegetation surfaces that are intensively managed and directly influenced by the artificial cover. Land cover composition as detailed as construction material abundance (Herold et al 2003;Roessner et al 2001), vegetation characteristics such as type, structure or condition (Alonzo et al 2015;Degerickx et al 2018;Delegido et al 2014;Pontius et al 2017), information on vertical structures and surface roughness (Zhou et al 2005), water run-off potentials (Weng 2001) or spatial-temporal surface temperature changes (Deng and Wu 2013;Imhoff et al 2010) can be deduced when the full spectrum of remote sensing technology is considered, including multi-and hyperspectral optical sensors, thermal sensors, microwave and lidar systems (Small et al 2018). This way, ecologically relevant information such as spatial patterns of impervious and vegetated surface, biomass estimates and inputs for microclimatic or hydrological models can be derived (Carlson and Arthur 2000;Heldens et al 2017;Huang et al 2013;Ngie et al 2014;Voogt and Oke 2003).…”

Section: Introductionmentioning

confidence: 99%

Imaging Spectroscopy of Urban Environments

et al. 2018

Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for environmental models will be provided, for example, for microclimate or hydrological models. The diverse vertical structures, highly frequent spatial change and a great variety of materials cause challenges for urban environmental mapping with Earth observation data, especially at the 30 m spatial resolution of data from future spaceborne imaging spectrometers. This paper gives an overview of the state-of-the-art in urban imaging spectroscopy considering decreasing spatial resolution, the related user requirements and existing knowledge gaps, as well as expected future directions for the work with new data sets.