HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS

In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping. The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).

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A New Crop Spectral Signatures Database Interactive Tool (CSSIT)

Data ◽

10.3390/data4020077 ◽

2019 ◽

Vol 4 (2) ◽

pp. 77 ◽

Cited By ~ 1

Author(s):

Mohamad M. Awad ◽

Bassem Alawar ◽

Rana Jbeily

Keyword(s):

Eastern Mediterranean ◽

Decision Makers ◽

High Spectral Resolution ◽

High Temporal Resolution ◽

Growth Stages ◽

Area Index ◽

Spectral Signatures ◽

Agriculture Sector ◽

Interactive Tool ◽

Physical And Chemical

In many countries, commodities provided by the agriculture sector play an important role in the economy. Securing food is one aspect of this role, which can be achieved when the decision makers are supported by tools. The need for cheap, fast, and accurate tools with high temporal resolution and global coverage has encouraged the decision makers to use remote sensing technologies. Field spectroradiometer with high spectral resolution can substantially improve crop mapping by reducing similarities between different crop types that have similar ecological conditions. This is done by recording fine details of the crop interaction with sunlight. These details can increase the same crop recognition even with the variation in the crop chemistry and structure. This paper presents a new spectral signatures database interactive tool (CSSIT) for the major crops in the Eastern Mediterranean Basin such as wheat and potato. The CSSIT’s database combines different data such as spectral signatures for different periods of crop growth stages and many physical and chemical parameters for crops such as leaf area index (LAI) and chlorophyll-a content (CHC). In addition, the CSSIT includes functions for calculating indices from spectral signatures for a specific crop and user interactive dialog boxes for displaying spectral signatures of a specific crop at a specific period of time.

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Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

Remote Sensing ◽

10.3390/rs12030408 ◽

2020 ◽

Vol 12 (3) ◽

pp. 408

Author(s):

Małgorzata Krówczyńska ◽

Edwin Raczko ◽

Natalia Staniszewska ◽

Ewa Wilk

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Spectral Feature ◽

Hyperspectral Data ◽

Aerial Photographs ◽

Support Vector ◽

Spectral Angle Mapper ◽

Asbestos Cement ◽

Feature Fitting

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

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NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-593-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 593-598

Author(s):

S. Priya ◽

R. Ghosh ◽

B. K. Bhattacharya

Keyword(s):

Remote Sensing ◽

Dimensionality Reduction ◽

Infrared Imaging ◽

Hyperspectral Image ◽

Signal To Noise Ratio ◽

Hyperspectral Data ◽

Correlation Technique ◽

Spectral Correlation ◽

Spectral Angle Mapper ◽

Non Linear

<p><strong>Abstract.</strong> Hyperspectral remote sensing is an advanced remote sensing technology that enhances the ability of accurate classification due to presence of narrow contiguous bands. The large number of continuous bands present in hyperspectral data introduces the problem of computational complexity due to presence of redundant information. There is a need for dimensionality reduction to enhance the ability of users for better characterization of features. Due to presence of high spectral correlation in the hyperspectral datasets, optimum de-correlation technique is required which transforms the hyperspectral data to lower dimensions without compromising with the desirable information present in the data. In this paper, focus has been to reduce the spectral dimensionality problem. So, this research aimed to develop computationally efficient non-linear autoencoder algorithm taking the advantage of non-linear properties of hyperspectral data. The proposed algorithm was applied on airborne hyperspectral image of Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) over Anand region of Gujarat and the performance of the algorithm was evaluated. The Signal-to-Noise Ratio (SNR) increased from 22.78 dB to 48.48 dB with increase in number of nodes in bottleneck layer for reconstruction of image. Spectral distortion was also measured using Spectral Angle Mapper Algorithm (SAM), which reduced from 0.38 to 0.05 with increase in number of nodes in bottleneck layer up to 10. So, this algorithm was able to give good reconstruction of original image from the nodes present in the bottleneck layer.</p>

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A HYPERSPECTRAL BASED METHOD TO DETECT CANNABIS PLANTATION IN INACCESSIBLE AREAS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-547-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 547-551

Author(s):

M. Houmi ◽

B. Mohamadi ◽

T. Balz

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Hyperspectral Data ◽

Spectral Signature ◽

Spectral Angle Mapper ◽

Planting Methods ◽

Maximum Angle ◽

Hyperion Data ◽

Remote Sensing Techniques ◽

Two Stages

The increase in drug use worldwide has led to sophisticated illegal planting methods. Most countries depend on helicopters, and local knowledge to identify such illegal plantations. However, remote sensing techniques can provide special advantages for monitoring the extent of illegal drug production. This paper sought to assess the ability of the Satellite remote sensing to detect Cannabis plantations. This was achieved in two stages: 1- Preprocessing of Hyperspectral data EO-1, and testing the capability to collect the spectral signature of Cannabis in different sites of the study area (Morocco) from well-known Cannabis plantation fields. 2- Applying the method of Spectral Angle Mapper (SAM) based on a specific angle threshold on Hyperion data EO-1 in well-known Cannabis plantation sites, and other sites with negative Cannabis plantation in another study area (Algeria), to avoid any false Cannabis detection using these spectra. This study emphasizes the benefits of using hyperspectral remote sensing data as an effective detection tool for illegal Cannabis plantation in inaccessible areas based on SAM classification method with a maximum angle (radians) less than 0.03.

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New Hyperspectral Procedure to Discriminate Intertidal Macroalgae

Remote Sensing ◽

10.3390/rs14020346 ◽

2022 ◽

Vol 14 (2) ◽

pp. 346

Author(s):

Florian Douay ◽

Charles Verpoorter ◽

Gwendoline Duong ◽

Nicolas Spilmont ◽

François Gevaert

Keyword(s):

Spectral Response ◽

Hyperspectral Data ◽

Species Level ◽

Embedded Sensors ◽

Viewing Angle ◽

Species Classification ◽

Spectral Angle Mapper ◽

New Approach ◽

Classification Approach ◽

First Derivative

The recent development and miniaturization of hyperspectral sensors embedded in drones has allowed the acquisition of hyperspectral images with high spectral and spatial resolution. The characteristics of both the embedded sensors and drones (viewing angle, flying altitude, resolution) create opportunities to consider the use of hyperspectral imagery to map and monitor macroalgae communities. In general, the overflight of the areas to be mapped is conconmittently associated accompanied with measurements carried out in the field to acquire the spectra of previously identified objects. An alternative to these simultaneous acquisitions is to use a hyperspectral library made up of pure spectra of the different species in place, that would spare field acquisition of spectra during each flight. However, the use of such a technique requires developed appropriate procedure for testing the level of species classification that can be achieved, as well as the reproducibility of the classification over time. This study presents a novel classification approach based on the use of reflectance spectra of macroalgae acquired in controlled conditions. This overall approach developed is based on both the use of the spectral angle mapper (SAM) algorithm applied on first derivative hyperspectral data. The efficiency of this approach has been tested on a hyperspectral library composed of 16 macroalgae species, and its temporal reproducibility has been tested on a monthly survey of the spectral response of different macro-algae species. In addition, the classification results obtained with this new approach were also compared to the results obtained through the use of the most recent and robust procedure published. The classification obtained shows that the developed approach allows to perfectly discriminate the different phyla, whatever the period. At the species level, the classification approach is less effective when the individuals studied belong to phylogenetically close species (i.e., Fucus spiralis and Fucus serratus).

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CROP MAPPING USING HYPERSPECTRAL DATA AND TECHNOLOGIES - A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS

18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing ◽

10.5593/sgem2018/2.3/s10.012 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mohamad Awad

Keyword(s):

Hyperspectral Data ◽

Supervised Segmentation ◽

Segmentation Algorithms ◽

Crop Mapping

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Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-667-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 667-670 ◽

Cited By ~ 5

Author(s):

R. Vidhya ◽

D. Vijayasekaran ◽

M. Ahamed Farook ◽

S. Jai ◽

M. Rohini ◽

...

Keyword(s):

Remote Sensing ◽

Health Status ◽

Remote Sensing Data ◽

Principal Component ◽

Hyperspectral Remote Sensing ◽

Hyperspectral Data ◽

Support Vector ◽

Spectral Angle Mapper ◽

Svm Classification ◽

Sensing Data

Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

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Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

Remote Sensing ◽

10.3390/rs13112125 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2125

Author(s):

Bardia Yousefi ◽

Clemente Ibarra-Castanedo ◽

Martin Chamberland ◽

Xavier P. V. Maldague ◽

Georges Beaudoin

Keyword(s):

Matched Filter ◽

Principal Component ◽

Hyperspectral Data ◽

Clustering Methods ◽

Spectral Angle Mapper ◽

Long Wave ◽

Clustering Approach ◽

Spectral Comparison ◽

Computational Simplicity ◽

Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

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