Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels

The prospect of regular assessments of insect defoliation using remote sensing technologies has increased in recent years through advances in the understanding of the spectral reflectance properties of vegetation. The aim of the present study was to evaluate the ability of the red edge channel of Rapideye imagery to discriminate different levels of insect defoliation in an African savanna by comparing the results of obtained from two classifiers. Random Forest and Support vector machine classification algorithms were applied using different sets of spectral analysis involving the red edge band. Results show that the integration of information from red edge increases classification accuracy of insect defoliation levels in all analysis performed in the study. For instance, when all the 5 bands of Rapideye imagery were used for classification, the overall accuracies increases about 19% and 21% for SVM and RF, respectively, as opposed to when the red edge channel was excluded. We also found out that the normalized difference red-edge index yielded a better accuracy result than normalized difference vegetation index. We conclude that the red-edge channel of relatively affordable and readily available high-resolution multispectral satellite data such as Rapideye has the potential to considerably improve insect defoliation classification especially in sub-Saharan Africa where data availability is limited.     

Employing airborne multispectral digital imagery to map Brazilian pepper infestation in South Texas

A study was conducted in south Texas to determine the feasibility of using airborne multispectral digital imagery for differentiating the invasive plant Brazilian pepper (Schinus terebinthifolius) from other cover types. Imagery obtained in the visible, near-infrared, and mid-infrared regions of the light spectrum and a supervised classification approach were employed to develop thematic maps of two areas infested with Brazilian pepper. Map accuracies ranged from 84.2 to 100% for the Brazilian pepper class. Findings support using airborne multispectral digital imagery as a tool for separating Brazilian pepper from associated land cover types and further encourage exploration of airborne multispectral digital imagery and image processing techniques for developing maps of Brazilian pepper infestation in Texas and abroad.     

利用多源遥感数据进行城市不透水面覆盖度估算

利用深圳实验区4种不同传感器获取的遥感数据,通过CART算法进行城市ISP估算。讨论了多光谱遥感数据的不同波段在ISP估算中的重要性,比较了针对三种不同中分辨率影像建立的ISP估算模型在性能上的差异。实验结果表明,近红外波段对ISP估算结果的贡献最大,具有较高空间分辨率和成像辐射质量的遥感影像得到的估算结果精度较高,所有的估算结果均在实际ISP分布范围的两端分别存在着高估和低估的现象。     

The 13th Asian conference on remote sensing,Mongolia

Abstract

Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi‐layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18^th, 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co‐occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co‐occurrence texture and spectral signatures combined, and a thirteen‐class multi‐layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per‐plot classification accuracies by 15.7%, compared to the use of the spectral data alone.     

A high resolution multispectral video system

The design, operation, and testing of a high resolution multispectral video system (HRMVS) is described. The system uses state‐of‐the‐art video technology. It incorporates three black‐and‐white (B & W) visible/near‐infrared (NIR) (0.4–1.1 μm) light sensitive solid‐state cameras equipped with band‐pass filters and provides two kinds of simultaneously synchronized video images: (1) color‐infrared (CIR) composite imagery and (2) its three‐band B & W image components. Only CIR composite imagery is presented here with its B & W components (yellow‐green, red, and NIR bands), but any false color combination can be generated by the encoder. Images are recorded on high resolution (400 horizontal lines) Super (S)‐VHS recorders. An independent solid‐state conventional color (0.4–0.7 μm) camcorder (S‐VHS) was optional to this system. It was set up to acquire imagery at approximately the same field‐of‐view as the three‐camera synchronized system. Examples of imagery of various natural resource characteristics are given. Color‐infrared composite imagery had similar color tonal renditions to that of CIR film. The high resolution multispectral Black & White B & W images showed that some terrain features could be discriminated better in certain bands. For example, the yellow‐green (543–0.552 μm) band was best for distinguishing chlorosis in grain sorghum (Sorghum bicolor Moench), whereas the NIR band was optimum for separating biomass levels in alfalfa (Medicago sativa L.). The color and Black & White B & W multispectral image results showed this system to be a valuable and versatile tool for a variety of remote sensing applications.     

A six-camera digital video imaging system sensitive to visible,red edge,near-infrared,and mid-infrared wavelengths

This paper describes a six-camera multispectral digital video imaging system designed for natural resource assessment and shows its potential as a research tool. It has five visible to near-infrared light sensitive cameras, one near-infrared to mid-infrared light sensitive camera, a monitor, a computer with a multichannel digitizing board, a keyboard, a power distributor, an amplifier, and a mouse. Each camera is fitted with a narrowband interference filter, allowing the system to obtain imagery in the blue (447 – 455 nm), green (555 – 565 nm), red (625 – 635 nm), red edge (704 – 716 nm), near-infrared (814–826 nm), and mid-infrared (1631 – 1676 nm) regions of the electromagnetic spectrum. Analogue video acquired by this system is converted to digital format. Radiometric resolution of the imagery is 8-bit (pixel values range 0 – 255). Images obtained by the system can be evaluated individually and/or in combination with each other to assess natural resources.     

INTERPRETATION OF THE AGRICULTURAL LANDSCAPE WITH SPACE IMAGERY

Heterochronous multispectral imagery from the “Fragment” scanner is used to identify and map a series of “natural-agricultural complexes,” or agricultural landscapes, of southern European Russia. Interpretation is based on imagery in the green, orange, red, and near-infrared zones of the spectrum (0.5-1.1 μm). Interpretation keys and other information designed to facilitate feature discrimination (optimal wavelengths, best seasons for imagining) are provided in many instances. Natural landscape and soil erosion maps are also compiled from the imagery, which supply information (an optimal crop rotation scheme and needed reclamation measures) used on the agricultural landscape map. Translated from: Vestnik Moskovs-kogo Unlversiteta, geografiya, 1985, No. 2, pp. 34-41.     

南海区域卫星多光谱影像太阳耀斑消除方法

太阳光线被水面直接反射到传感器会造成耀斑污染的现象。高分辨率卫星影像可利用近红外波段消除太阳耀斑。在分析耀斑产生的辐射传输过程的基础上,利用World View-2卫星4波段和8波段多光谱影像设计实验,比较了3种常见的高分辨率太阳耀斑消除算法。结果表明,各种耀斑消除算法的目视效果都较为优秀,定量应用场合下Hedly算法和Goodman算法更有优势。     

Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods

Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier.     

Texture-based classification of sub-Antarctic vegetation communities on Heard Island

This study was the first to use high-resolution IKONOS imagery to classify vegetation communities on sub-Antarctic Heard Island. We focused on the use of texture measures, in addition to standard multispectral information, to improve the classification of sub-Antarctic vegetation communities. Heard Island’s pristine and rapidly changing environment makes it a relevant and exciting location to study the regional effects of climate change. This study uses IKONOS imagery to provide automated, up-to-date, and non-invasive means to map vegetation as an important indicator for environmental change. Three classification techniques were compared: multispectral classification, texture based classification, and a combination of both. Texture features were calculated using the Grey Level Co-occurrence Matrix (GLCM). We investigated the effect of the texture window size on classification accuracy. The combined approach produced a higher accuracy than using multispectral bands alone. It was also found that the selection of GLCM texture features is critical. The highest accuracy (85%) was produced using all original spectral bands and three uncorrelated texture features. Incorporating texture improved classification accuracy by 6%.     

Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests

Tree species composition of forest stand is an important indicator of forest inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying forest biodiversity. In this study, we acquired high spatial resolution multispectral and RGB imagery over a subtropical natural forest in southwest China using a fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral and RGB derived point clouds, upon which individual tree crown (ITC) delineation algorithms and a machine learning classifier were used to identify dominant tree species. To do so, the structure-from-motion method was used to generate RGB imagery-based DAP point clouds. Then, three ITC delineation algorithms (i.e., point cloud segmentation (PCS), image-based multiresolution segmentation (IMRS), and advanced multiresolution segmentation (AMRS)) were used and assessed for ITC detection. Finally, tree-level metrics (i.e., multispectral, texture and point cloud metrics) were used as metrics in the random forest classifier used to classify eight dominant tree species. Results indicated that the accuracy of the AMRS ITC segmentation was highest (F₁-score = 82.5 %), followed by the segmentation using PCS (F₁-score = 79.6 %), the IMRS exhibited the lowest accuracy (F₁-score = 78.6 %); forest types classification (coniferous and deciduous) had a higher accuracy than the classification of all eight tree species, and the combination of spectral, texture and structural metrics had the highest classification accuracy (overall accuracy = 80.20 %). In the classification of both eight tree species and two forest types, the classification accuracies were lowest when only using spectral metrics, indicated that the texture metrics and point cloud structural metrics had a positive impact on the classification (the overall accuracy and kappa accuracy increased by 1.49–4.46 % and 2.86–6.84 %, respectively).     

结合多光谱影像降维与深度学习的城市单木树冠检测

多光谱数据的降维处理对基于深度学习的单木树冠检测研究有重要意义,如何使用合适的降维方法以提高单木检测的精度却少有研究讨论。本文使用无人机搭载多光谱相机进行航拍作业,采集研究区内银杏树种多光谱影像。将原始多光谱影像通过特征波段选择、特征提取、波段组合的方法生成5种不同的数据集用于训练3种经典的深度学习网络FPN-Faster-R-CNN,YOLOv3,Faster R-CNN。其中由波段组合方法得到的近红外、红色、绿色波段组合在不同类型的目标检测网络中都有最好的检测结果,其中FPN-Faster-R-CNN网络对银杏树冠的检测精度最高为88.4%,由OIF指标得到的蓝色、红色、近红外波段组合信息量最高,但在所有网络中的平均检测精度最低,仅为79.3%。实验结果表明：在不同波段降维方法中,若降维后的影像中目标物体的色彩与背景差异较明显,且轮廓清晰,则深度学习网络对树冠的检测可获得较好的结果。而影像自身的信息量则对深度学习网络的树冠检测能力的提升作用有限。本研究中针对多光谱影像的降维方法分析,为基于深度学习的单木树冠检测研究提供了重要的实验参考。     

利用融合方法提升WorldVeiw短波红外影像分辨率

WorldView卫星在8个可见光-近红外多光谱波段的基础上,新增加的8个短波红外(简称SWIR)影像,大大提高了地物信息提取能力.但短波红外影像分辨率与多光谱影像相比分辨率过低,影响应用效果.本文提出了一种结合主分量变换和非下采样小波变换的影像融合方法来提升WorldView短波红外影像的空间分辨率.定量指标和目视评价证明本文提出的融合方法具有较好的融合效果,能够在显著提升短波红外影像空间分辨率的同时很好地保持原始光谱特性.     

利用融合方法提升WorldView短波红外影像分辨率

WorldView卫星在8个可见光-近红外多光谱波段的基础上,新增加的8个短波红外（简称SWIR）影像,大大提高了地物信息提取能力。但短波红外影像分辨率与多光谱影像相比分辨率过低,影响应用效果。本文提出了一种结合主分量变换和非下采样小波变换的影像融合方法来提升WorldView短波红外影像的空间分辨率。定量指标和目视评价证明本文提出的融合方法具有较好的融合效果,能够在显著提升短波红外影像空间分辨率的同时很好地保持原始光谱特性。     

Mapping trees in high resolution imagery across large areas using locally variable thresholds guided by medium resolution tree maps

Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists.     

Texture augmented detection of macrophyte species using decision trees

Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 × 5 and 13 × 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier.     

Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

大兴安岭林区无人机可见光影像散发枯立木识别算法

枯立木识别对森林资源管理,生物多样性保护,以及森林碳储量变化评估具有重要价值.无人机高分辨率影像为枯立木调查提供了较为便捷的方式.现有枯立木识别算法多依靠拥有红边、近红外波段的多光谱影像来实现.相比于多光谱相机,消费级无人机通常搭载的是用于获取可见光(RGB)影像的普通数码相机,较少的波段信息为基于RGB影像的枯立木自...     

Comparative spatio-spectral heterogeneity analysis using multispectral and hyperspectral airborne images

Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible, near-infrared and short wave infrared spectra is important. Till now, little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images, especially on both multispectral and hyperspectral airborne images. In this study, four airborne images, Airborne Thematic Mapper, Compact Airborne Spectrographic Imager, Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood, UK, were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling. Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength. Within the visible, near-infrared spectra and short wave infrared spectra, greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra. There are dramatic changes across the red and red edge spectra, and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively. In all, for real multisensor airborne images, the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength. Besides, if with close spatial resolution, the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved. A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.     

高分辨率卫星立体双介质浅水水深测量方法

提出了一种基于高分辨率卫星多光谱立体像对的浅水水深测量方法。该方法利用多光谱近红波段消除太阳耀斑,以有理函数模型(RFM)构建测区原始DEM,通过水陆边界内插获得水面高程,并采用双介质近似折射改正模型消除目标点垂直坐标偏移。试验表明,本文的模型方法和处理流程在水面平静、底质纹理丰富的浅海岛礁水深反演中能取得优于20%的相对测深精度,可为浅水水深测量提供新手段。