Remote sensing measurements in creating thematic spectral library

2018 ◽  
Author(s):  
Denitsa Borisova ◽  
Doyno Petkov ◽  
Roumen Nedkov ◽  
Hristo Nikolov ◽  
Ventzeslav Dimitrov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Library

scholarly journals Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

2018 ◽  
Vol 10 (12) ◽  
pp. 2027 ◽  
Author(s):  
Itiya Aneece ◽  
Prasad Thenkabail
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
The United States ◽  
Google Earth ◽  
Growth Stages ◽  
Agricultural Crops ◽  
Spectral Library ◽  
Crop Types ◽  
Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

The Methods of Mapping Lithology with AVIRIS Data

2014 ◽  
Vol 955-959 ◽  
pp. 3879-3882
Author(s):  
Yan Fang Ming ◽  
Li Yang
Keyword(s):  
Remote Sensing ◽  
Measurement Data ◽  
Absorption Index ◽  
Mining District ◽  
Spectral Absorption ◽  
Spectral Library ◽  
Hyper Spectral ◽  
Ground Measurement ◽  
Spectral Angle

A hyper-spectral remote sensing instrument AVIRIS was used to map lithology. Ground measurement data of lithology from ASTER Spectral Library were used to analyze the characters of the spectrum and form the model to estimate the type of lithologies. we process the spectrum with the methods of spectral angle mapping, and spectral absorption index etc. To enhance the significance of the spectrum character, we used the methods of spectral angle mapping, and spectral absorption index etc to process the spectrum. An AVIRIS data covers the Cuprite mining district in western Nevada, USA was used to do the experiment, result shows that the lithologies got from the AVIRIS have a high consistence with groud measurement.

Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm

2004 ◽  
Vol 91 (3-4) ◽  
pp. 304-319 ◽  
Author(s):  
Martin Herold ◽  
Dar A Roberts ◽  
Margaret E Gardner ◽  
Philip E Dennison
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Spectral Library

scholarly journals HYPERSPECTRAL REMOTE SENSING FOR TEMPERATE HORTICULTURE FRUIT CROPS IN NORTHERN-WESTERN HIMALAYAN REGION: A REVIEW

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 333-338
Author(s):  
P. Upadhyay ◽  
D. Uniyal ◽  
M. P. S. Bisht
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Himalayan Region ◽  
Specific Class ◽  
Fruit Crops ◽  
Spectral Library ◽  
Indian States ◽  
The North ◽  
Hyper Spectral ◽  
North Western

<p><strong>Abstract.</strong> The North-Western Indian States and the North-Eastern Indian States of Indian Himalayan Region (IHR) are rich of various temperate horticulture fruits such as the Apple, Pear, Peach, Plum, Apricot, Sweet Cherry and Sour Cherry. These horticulture fruits are majorly grown in North-western region comprising of Jammu and Kashmir (J&amp;amp;K), Himachal Pradesh (H.P.) and Uttarakhand (U.K.). These states of IHR share the same type of geographical and climatic condition and having nearly common flora and fauna. Out of the various horticulture temperate fruit crops apple and apricot have the potential to make a positive impact on economy of these states. Hyper-spectral remote sensing due to its capability of identifying the small variations within a particular feature (or land cover) is an important tool for discriminating or mapping the specific land cover among the various existing classes. Contrary to multispectral remote sensing, it is not only capable of mapping the vegetation class among the various classes in the land but also has the potential to discriminate within the different classes of vegetation as well as diseases identification within a class. This specific class level discrimination of vegetation is an important tool for mapping. In hyper-spectral remote sensing this variation is observed through the possible discrimination of spectral signatures of various vegetation classes. Thus, due to its fine spectral bands this type of remote sensing data has the potential to map the horticulture crops. However, the processing of hyper-spectral data always require the in-situ measurements or existing spectral library. Such a type of spectral library is never generated for the horticulture crops of IHR. This can be further useful for identifying the disease affected crops and input for developing model for estimation of biophysical and biochemical parameters. Therefore, in this study, a need for the development of spectral library for temperate horticulture crop has been highlighted. Further, a methodology for the processing of hyperspectral data has also be proposed.</p>

scholarly journals Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network

2021 ◽  
Vol 13 (16) ◽  
pp. 3319
Author(s):  
Nan Ma ◽  
Lin Sun ◽  
Chenghu Zhou ◽  
Yawen He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Spectral Library ◽  
Remote Sensing Images ◽  
Different Types ◽  
Sentinel 2

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.

scholarly journals Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 496 ◽  
Author(s):  
Zhang ◽  
He ◽  
Yuan ◽  
Liu ◽  
Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Tea Plant ◽  
Support Vector ◽  
Growth Monitoring ◽  
Spectral Features ◽  
Spectral Library ◽  
Status Monitoring ◽  
Rice Growth ◽  
Crop Classification

The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.

scholarly journals Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula

2021 ◽  
Vol 13 (13) ◽  
pp. 2470
Author(s):  
Junhwa Chi ◽  
Hyoungseok Lee ◽  
Soon Gyu Hong ◽  
Hyun-Cheol Kim
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Spectral Unmixing ◽  
Polar Regions ◽  
Spectral Library ◽  
Sensing Data ◽  
Barton Peninsula ◽  
Shortwave Infrared

Spectral information is a proxy for understanding the characteristics of ground targets without a potentially disruptive contact. A spectral library is a collection of this information and serves as reference data in remote sensing analyses. Although widely used, data of this type for most ground objects in polar regions are notably absent. Remote sensing data are widely used in polar research because they can provide helpful information for difficult-to-access or extensive areas. However, a lack of ground truth hinders remote sensing efforts. Accordingly, a spectral library was developed for 16 common vegetation species and decayed moss in the ice-free areas of Antarctica using a field spectrometer. In particular, the relative importance of shortwave infrared wavelengths in identifying Antarctic vegetation using spectral similarity comparisons was demonstrated. Due to the lack of available remote sensing images of the study area, simulated images were generated using the developed spectral library. Then, these images were used to evaluate the potential performance of the classification and spectral unmixing according to spectral resolution. We believe that the developed library will enhance our understanding of Antarctic vegetation and will assist in the analysis of various remote sensing data.

scholarly journals KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials

2019 ◽  
Vol 11 (18) ◽  
pp. 2149
Author(s):  
Rebecca Ilehag ◽  
Andreas Schenk ◽  
Yilin Huang ◽  
Stefan Hinz
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Building Materials ◽  
Near Infrared ◽  
Imaging Spectroscopy ◽  
Short Wave ◽  
Spectral Library ◽  
Spectral Libraries ◽  
Roofing Materials

Knowledge about the existing materials in urban areas has, in recent times, increased in importance. With the use of imaging spectroscopy and hyperspectral remote sensing techniques, it is possible to measure and collect the spectra of urban materials. Most spectral libraries consist of either spectra acquired indoors in a controlled lab environment or of spectra from afar using airborne systems accompanied with in situ measurements. Furthermore, most publicly available spectral libraries have, so far, not focused on facade materials but on roofing materials, roads, and pavements. In this study, we present an urban spectral library consisting of collected in situ material spectra with imaging spectroscopy techniques in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) spectral range, with particular focus on facade materials and material variation. The spectral library consists of building materials, such as facade and roofing materials, in addition to surrounding ground material, but with a focus on facades. This novelty is beneficial to the community as there is a shift to oblique-viewed Unmanned Aerial Vehicle (UAV)-based remote sensing and thus, there is a need for new types of spectral libraries. The post-processing consists partly of an intra-set solar irradiance correction and recalculation of reference spectra caused by signal clipping. Furthermore, the clustering of the acquired spectra was performed and evaluated using spectral measures, including Spectral Angle and a modified Spectral Gradient Angle. To confirm and compare the material classes, we used samples from publicly available spectral libraries. The final material classification scheme is based on a hierarchy with subclasses, which enables a spectral library with a larger material variation and offers the possibility to perform a more refined material analysis. The analysis reveals that the color and the surface structure, texture or coating of a material plays a significantly larger role than what has been presented so far. The samples and their corresponding detailed metadata can be found in the Karlsruhe Library of Urban Materials (KLUM) archive.

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