Hyperspectral classification applied to the Belgian coastline

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Research Article| August 01, 2014 Effect of Ground Covers on Soil Temperature in Urban and Rural Areas JING-HONG WU; JING-HONG WU School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210093, China Search for other works by this author on: GSW Google Scholar CHAO-SHENG TANG; CHAO-SHENG TANG School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210093, China Search for other works by this author on: GSW Google Scholar BIN SHI; BIN SHI 1 School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210093, China 1Corresponding author email: shibin@nju.edu.cn. Search for other works by this author on: GSW Google Scholar LEI GAO; LEI GAO College of Civil Engineering and Transportation, Hohai University, Nanjing, 210093, China Search for other works by this author on: GSW Google Scholar HONG-TAO JIANG; HONG-TAO JIANG School of Geographic and Oceanographic Science, Nanjing University, Nanjing, 210093, China Search for other works by this author on: GSW Google Scholar JOHN L DANIELS JOHN L DANIELS Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA Search for other works by this author on: GSW Google Scholar Environmental & Engineering Geoscience (2014) 20 (3): 225–237. https://doi.org/10.2113/gseegeosci.20.3.225 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation JING-HONG WU, CHAO-SHENG TANG, BIN SHI, LEI GAO, HONG-TAO JIANG, JOHN L DANIELS; Effect of Ground Covers on Soil Temperature in Urban and Rural Areas. Environmental & Engineering Geoscience 2014;; 20 (3): 225–237. doi: https://doi.org/10.2113/gseegeosci.20.3.225 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Abstract Human activity has changed land surfaces by converting native ecosystems to urban land uses, which has caused increases in soil temperature. In order to understand the effect of different ground covers on soil temperature, observation stations were built under concrete, bare soil, and grass ground covers located in urban and rural areas of Nanjing City, China. By analyzing continuously recorded soil temperature data from June 2009 to June 2010, it was found that the average daily soil temperature in both urban and rural areas shows a tendency of concrete > bare soil > grass ground cover. Soil temperature under concrete ground cover in summer is significantly higher than that under bare soil and grass, consistent with the urban heat island effect. The cover thermal-physical properties and soil structures lead to differences in soil temperature between urban and rural areas. As compared to bare or grass-covered soils, concrete-covered soils exhibit lower values of evaporation rate, reflectivity, and specific heat capacity and higher values of thermal conductivity. As such, the initial heating rate of the soil under concrete ground cover is faster, and the final temperature is higher. These findings provide a basis with which to reduce the heat island effect through the provision of increasing grass-covered open space. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

As an important indicator of anthropogenic impacts on the Earth’s surface, it is of great necessity to accurately map large-scale urbanized areas for various science and policy applications. Although spectral mixture analysis (SMA) can provide spatial distribution and quantitative fractions for better representations of urban areas, this technique is rarely explored with 1-km resolution imagery. This is due mainly to the absence of image endmembers associated with the mixed pixel problem. Consequently, as the most profound source of error in SMA, endmember variability has rarely been considered with coarse resolution imagery. These issues can be acute for fractional land cover mapping due to the significant spectral variations of numerous land covers across a large study area. To solve these two problems, a hierarchically object-based SMA (HOBSMA) was developed (1) to extrapolate local endmembers for regional spectral library construction; and (2) to incorporate endmember variability into linear spectral unmixing of MODIS 1-km imagery for large-scale impervious surface abundance mapping. Results show that by integrating spatial constraints from object-based image segments and endmember extrapolation techniques into multiple endmember SMA (MESMA) of coarse resolution imagery, HOBSMA improves the discriminations between urban impervious surfaces and other land covers with well-known spectral confusions (e.g., bare soil and water), and particularly provides satisfactory representations of urban fringe areas and small settlements. HOBSMA yields promising abundance results at the km-level scale with relatively high precision and small bias, which considerably outperforms the traditional simple mixing model and the aggregated MODIS land cover classification product.

Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra

A mobile device with embedded cognitive radios (CRs) is integrated into a novel low-cost massive antenna configuration multihop or device-to-device (D2D) wireless network that temporarily uses any portion of the spectrum for transmissions. However, the number of spectral bands (SBs) that a CR can sense within a specific time is limited. Furthermore, CR node transmissions interfere with one another. This paper proposes a method both for cooperative sensing and transmission with preallocated disjoining of spectrum sections (SSs) from neighboring links to increase the likelihood of matching available SBs. This minimizes sensing and negotiating overheads and reduces interference from a viewpoint of high-level graph model and mathematical formulation. The multiple-constraint routing with the resource allocation for cooperative sensing problem is proven to be NP-hard. The resource allocation method is compared with existing SB postallocation methods (i.e., random SB allocation and fixed SB allocation) and preallocation methods (i.e., link-based SS allocation and multiple parallel SB allocation). The proposed methods handle the resource preallocation mechanism with a centralized solution but handle the sensed results with a decentralized solution to reduce the negotiating and selecting time. We evaluate the performance of the system capacity, end-to-end transmission delay, fairness index, and standard deviation, thereby verifying the quality of the proposed method in terms of interferences, the fading effect, and other environment-dependent conditions. The results of the proposed method are more favorable than those of other methods.

This paper addresses the feasibility of utilizing parametric projection pursuit (PPP) for dimensionality reduction of hyperspectral data. With hyperspectral data, the large number of spectral bands can be both a blessing and a curse. It is known that for an accurate classification, the number of required training data increases exponentially with the number of spectral bands. When the data consists of many spectral bands, it is often not feasible to collect enough data for classification purposes, so dimensionality reduction methods, such as PPP, have been proposed for preprocessing hyperspectral data. in the past, PPP has been applied to remotely sensed hyperspectral data with the number of spectral bands being limited to /spl sim/200. In that work, it was shown that PPP can reduce the dimensionality form 100's to 10's, while maintaining class separation for target detection purposes. In this paper, the authors will build on previous research of PPP and investigate the feasibility of applying PPP to dat with 1000's of spectral bands. The data will be collected using an ASD spectroradiometer, Also, another dimensionality reduction algorithm, which is called the Best Spectral Band Combination (BSBC) using Receiver Operating Characteristics (ROC) curves, is used to examine the reliability of the PPP algorithm.

On the usefulness of one-class classifier ensembles for decomposition of multi-class problems

Spectral Images (SI) are acquired at multiple wavelengths across the electromagnetic spectrum, providing information that enhances performance in tasks such as material segmentation and classification by resolving ambiguities inherent in RGB images. SI devices are designed to capture a large number of spectral bands, which increases both cost and acquisition time, thereby limiting their practical deployment. However, not all spectral bands contribute equally to task-specific performance. To address this issue, a Deep Spectral Band Selection (DSBS) framework is proposed for spectral imaging tasks. Unlike previous methods that emphasize the preservation of non-task-specific information, DSBS identifies the most informative bands for a given task by jointly training a fully differentiable band selector and a neural network within an end-to-end learning framework. The selection process is guided by a proposed bin function and a custom lp-norm regularization term to achieve the desired number of spectral bands. Experimental results in material segmentation and classification tasks indicate that DSBS outperforms state-of-the-art machine and deep learning methods.

With numerous and contiguous spectral bands acquired from visible light (400?1,000 nm) to (near) infrared (1,000?1,700 nm and over), hyperspectral imaging (HSI) can potentially identify different objects by detecting minor changes in temperature, moisture, and chemical content. As a result, HSI has been widely applied in a number of application areas, including remote sensing [1]. HSI data contains two-dimensional (2-D) spatial and one-dimensional spectral information, and naturally forms a three-dimensional (3-D) hypercube with a high spectral resolution in nanometers that enables robust discrimination of ground features. However, new challenges arise in dealing with extremely large data sets. For a hypercube with relatively small spatial dimension of 600 ? 400 pixels at 16 bits-per-band-per-pixel, the data volume becomes 120 MB for 250 spectral bands. In some cases, this large data volume can be linearly increased when multiple hypercubes are acquired across time to monitor system dynamics in consecutive time instants. When the ratio between the feature dimension (spectral bands) and the number of data samples (in vector-based pixels) is vastly different, high-dimensional data suffers from the well-known curse of dimensionality. For feature extraction and dimensionality reduction, principal components analysis (PCA) is widely used in HSI [2], where the number of extracted components is significantly reduced compared to the original feature dimension, i.e., the number of spectral bands. For effective analysis of large-scale data in HSI, conventional PCA faces three main challenges:

A few studies have recently been published on changes in land use/land cover (LU/LC) of Angolan Miombo forests, however, none have attempted to offer forest management solutions for degraded Miombo forests. Landscapes are witness to past and present natural and social processes influencing the environment, where each period in the past leaves footprints on the landscape’s development, which can be described by a continual decrease in forest area over time. The expansion of degraded areas from 2000 to 20017 began near urban areas where many Miombo forests have been eliminated or highly degraded, particularly in the southwest and northeast of the Huambo province. Large areas of degraded forests were observed along the Benguela railway (Caminho de ferro de Benguela). Our detailed analysis of the landcover map suggests that the impact has been devastating and there is no form of forest protection, which leads to unregulated exploitation. Descriptions of the Miombo forest dynamics are explained using height–diameter curves developed for different vegetation types that provide important insights about forest structures in the management zones. The height–diameter models differed for all vegetation types, and four management zones (MZ) were created based on a set of particular attributes. The vegetation types differed in each management zone, which included agricultural land and bare soil (MZ–E), grassland or savanna (MZ–C), open Miombo forests (MZ–B), and closed Miombo forests (Miombo forests). The four management zones were easily identified on the available maps and the height–diameter models developed represent a fundamental tool for future studies on forest planning.

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.

A new version of the Edwards–Slingo (ES) radiation scheme is developed using the correlated‐k distribution (CKD) method. The work is conducted based on the line‐by‐line radiative transfer scheme GENLN2. A dataset of new ozone absorption cross‐section in the ultraviolet spectrum and new oxygen collision‐induced continuum data have been implemented in both the GENLN2 and new ES schemes. In order to improve the efficiency of the ES scheme, a new technique is proposed in this work to optimize the k distribution and a new method is used to deal with the gaseous overlapping absorption in a spectral band. The accuracy of the scheme is improved by replacing the scaling function used in the ES scheme with a pre‐determined look‐up table for consideration of the pressure and temperature dependency of the absorption oefficients. The number of spectral bands and number of absorbing species are both increased in the new scheme for better resolution of the spectral variation of absorbing species, aerosols and clouds. However, this does not increase the computational cost. Instead, it is reduced substantially compared with the previous version of the code. The treatment of transmission in the short‐wave spectrum is improved by implementing the absorbing species of CH4, N2O and O2 collision‐induced continuum absorption which are not included in the ES scheme. New O3 absorption cross‐section data in the ultraviolet spectrum measured by the European Space Agency are used to generate the CKD spectral data in the short‐wave spectral bands. These data have a temperature dependency and better spectral resolution. The irradiance and heating rate determined by the new scheme are tested against the same variables determined by GENLN2. It has been shown that the new scheme produces results more accurate than the ES scheme. Copyright © 2011 Royal Meteorological Society

Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算 DOI: 10.5846/stxb201308142075 作者: 作者单位: 中国科学院遥感与数字地球研究所,中国科学院遥感与数字地球研究所,中国科学院遥感与数字地球研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技支撑计划(2011BAH23B04);国家科技重大专项(21-Y30B05-9001-13/15) Estimating fractional cover of photosynthetic vegetation and non-photosynthetic vegetation in the Xilingol steppe region with EO-1 hyperion data Author: Affiliation: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:掌握草原生态系统光合植被覆盖度(fPV)与非光合植被覆盖度(fNPV)时空动态对了解干旱半干旱草原生态系统特征(覆盖状况、火灾负载、载畜量、干扰及恢复等)及进行科学、有效地草地资源管理具有重要的意义。选取锡林郭勒典型草原为试验区,以Hypeiron高光谱数据为数据源,利用NDVI-CAI三元线性混合模型对试验区fPV和fNPV的时空动态分布进行了估算,并对不同端元选择方法(最小包含端元特征法、纯净象元指数法和实测法)对估算结果的影响进行了比较分析。研究结果表明,NDVI-CAI三元线性混合模型是同时估测锡林郭勒草原fPV和fNPV的有效方法,且估算的fPV和fNPV的季节变化与牧草的物候发育特征相吻合。不同端元选择方法对估算精度具有一定的影响,其中基于最小包含端元特征法提取端元进行估算的精度最高,fPV估算的均方根误差RMSE=4.57,估算精度EA=91.2%;fNPV估算的RMSE=5.90,EA=67.91%(样本数N=52)。 Abstract:Quantitative estimation of the spatial and temporal dynamics of the fractional cover of photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) in semi-arid grasslands is critical for understanding grassland conditions such as vegetation abundance, drought severity, fire fuel load, stocking rate, and disturbance events and recovery. It is also important for scientific grassland resource management. Over the past several decades, remote sensing has become an important tool for estimating the fractional cover of vegetation, which is a key descriptor of ecosystem function. However, most efforts have been devoted to the estimation of fPV rather than fNPV, although the latter is equally important, especially in arid and semi-arid ecosystems. This study describes a linear unmixing approach for estimating fPV and fNPV in the Xilingol steppe region with hyperspectral and field investigation data. Five Hyperion images acquired on April 4, May 20, July 27, August 30, and November 15 in 2012 and a field-measured spectral library were utilized to explore the spectral feature space of fPV and fNPV in order to validate the feasibility of a linear unmixing model. This model is based on two complementary spectral indices of vegetation that have been used in remote sensing analyses to discriminate green and dry vegetation from soils: the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Different end-member extraction methods, including the Minimum-Volume Enclosing (MVE) method, the Pixel Purity Index (PPI) method, and a field measurement method, were adopted to retrieve the end-member values of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil, respectively, from NDVI and CAI. Then, the influence of end-member extraction on the accuracy of the fPV and fNPV estimation was evaluated through comparison with field-measured fPV and fNPV values acquired from classifications performed on fisheye photos (N=52). Subsequently, the optimum unmixing strategy was utilized to retrieve the temporal dynamics of fPV and fNPV in a fenced area, where the grassland was not influenced by human activities, so that the usefulness of these fractional coverage indices could be validated by checking their consistency with the phenology of natural grassland. The result shows that the linear unmixing model based on NDVI and CAI was effective for estimating fPV and fNPV in the Xilingol steppe region. The NDVI-CAI feature space follows a triangular distribution, where the three vertexes represent photosynthetic vegetation, non-photosynthetic vegetation, and bare soil, meeting the essential requirements of the linear unmixing model. The estimation accuracy was different for the different end-member extraction methods. The MVE-based estimation had the highest accuracy, with estimated accuracy of 91.2% and 67.91% for fPV and fNPV, respectively, followed by the PPI-based estimation (91.0% and 59.5% for fPV and fNPV, respectively) and the field-measurement-based estimation (86.2% and 56.7% for fPV and fNPV, respectively). In general, the estimation accuracy was higher for fPV than fNPV, and the field-measured end-member performed worse than the image end-member, which was probably due to the inconsistency between the field-measured spec and the Hyperion spec. Additionally, the temporal dynamics of fPV and fNPV were confirmed to be consistent with the phenological seasonal change in natural grasslands. Therefore, the method proposed here can be used to monitor the temporal and spatial variations of fPV and fNPV in semi-arid grasslands. 参考文献 相似文献 引证文献

In this article, a new strategy is proposed for soft urban land-cover extraction based on mixed training data and Support Vector Machines (SVMs). The strategy is applied to soft classification of urban water cover using Landsat 8 multispectral data in Beijing. The results are validated with extensive manual mapping data and are compared with the results derived from a well-trained SVM and the linear spectral unmixing method using only pure samples. Our experimental results indicate that the proposed strategy works effectively in extracting the water cover in the urban image. The results are better than those obtained by SVM and linear spectral unmixing with only pure samples. Our findings demonstrate that the combination of image-based mixed training data and SVM enhances the strengths of kernel-based approaches for soft mapping urban land cover from imaging data. Therefore, the proposed workflow constitutes a new flexible and extendable approach to soft mapping urban land cover. Future work includes the construction of a comprehensive image-based spectral library for urban areas, and the testing of the proposed strategy on additional land-cover types in urban areas such as grass, tree, and bare soil.