A back-propagation neural network for mineralogical mapping from AVIRIS data

Imaging spectrometers have the potential to identify surface mineralogy based on the unique absorption features in pixel spectra. A back-propagation neural network (BPN) is introduced to classify Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) of the Cuprite mining district (Nevada) data into mineral maps. The results are compared with the traditional acquired surface mineralogy maps from spectral angle mapping (SAM). There is no misclassification for the training set in the case of BPN; however 17 percent misclassification occurs in SAM. The validation accuracy of the SAM is 69 percent, whereas BPN results in 86 per cent accuracy. The calibration accuracy of the BPN is higher than that of the SAM, suggesting that the training process of BPN is better than that of the SAM. The high classification accuracy obtained with the BPN can be explained by: (1)its ability to deal with complex relationships (e.g., 40 dimensions) and (2) the nature of the dataset, the minerals are highly concentrated and they are mostly represented by pure pixels. This paper demonstrates that BPN has superior classification ability when applied to imaging spectrometer data.Remote SensingImaging Science & Photographic TechnologySCI(E)EI29ARTICLE197-1102

Adaptive illumination source for multispectral vision system applied to material discrimination

A multispectral system based on a monochrome camera and an adaptive illumination source is presented in this paper. Its preliminary application is focused on material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach, in which the relevant wavelengths for the required discrimination task are selected in advance using a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based on Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illuminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illumination produces multispectral imaging with a simple monochrome camera, and cold illumination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry

Spectral and thermal mapping of desert surface sediments for agricultural development

A combination of multispectral, thermal and microwave data obtained from space and supported by ground measurements are used to investigate the surface sediment characteristics of a desert plain area in Egypt (El-Gallaba Plain, NW of Aswan). This plain once hosted an ancestral river system that is nowadays largely covered by aeolian and gravelly sands, and thus, only detectible with radar and thermal images. The methodology consists of extracting thermo-physical and textural parameters to guide and improve supervised spectral classification results. The results show that surface mineralogy (obtained from spectral information) correlates strongly with surface emissivity, whereas grain size and surface roughness strongly correlates with apparent thermal inertia. Furthermore, several broad strips of thermal cooling-anomalies are arranged in a linear fashion and diagonally crossing the alluvial basin. The sediments within these strips show very different textural, thermo-physical and compositional characteristics with respect to the surrounding areas suggesting that they were deposited under different depositional environments such as structurally controlled linear basins. These tectonic depressions were confirmed by ground penetrating radar and could be promising areas for groundwater accumulation and exploration enabling agricultural development in the El-Gallaba Plain of the Western Desert in Egypt

Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.Comment: Accepted by IGARSS 201

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host–pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management

Development of A Simple Method for Detecting Mangrove Using Free Open Source Software

Mangrove forests are becoming attractive natural charms and make everyone to take advantage of the existence of these coastal ecosystems both directly and indirectly. However, the condition of mangrove forests is threatened by their presence due to environmental factors around them. Sustainable mangrove monitoring efforts must always be increased to support the preservation of the mangrove ecosystem. The purpose of this study is to develop a fast and easy mangrove forest identification method based on remote sensing satellite imagery data. The research location chosen was the mangrove area in Segara Anakan, Cilacap. The data image used is Landsat 8 image acquisition on December 3, 2017 with path/row 121/065 obtained from the LAPAN Pustekdata Landsat catalog. The methods used include the Optimum Index Factor (OIF) method for selecting the best channels and the supervised classification method using the Semi-Automatic Classification Plugin (SCP) contained in open source software and provides three algorithm choices for the classification process including Minimum Distance, Maximum Likelihood and Spectral Angle Mapping. The results show the combination of RGB 564 (NIR+SWIR+RED) was the best in the identification of mangrove forests and the Maximum Likelihood classification algorithm was the most optimal in distinguishing mangrove and mangrove classes from both Macro Class and Class levels. The results of the calculation of the area show the mangrove area of 7,037.16 ha. The developed method can produce information on the distribution of mangroves at research sites more quickly, easily, effectively, and efficiently

The Influence of Seasonality on the Multi-Spectral Image Segmentation for Identification of Abandoned Land

Areas of agricultural land in Lithuania have decreased from 2005 to 2021 by up to 2.4%. Agricultural lands that are no longer used for their main purpose are very likely to become abandoned and the emergence of such lands can cause a variety of social, economic, and environmental problems. Therefore, it is very important to constantly monitor changes of abandoned agricultural lands. The purpose of the research is to analyse the influence of seasonality on image segmentation for the identification of abandoned land areas. Multi-spectral Sentinel-2 images from different periods (April, July, and September) and three supervised image segmentation methods (Spectral Angle Mapping (SAM), Maximum_Likelihood (ML), and Minimum distance (MD)) were used with the same parameters in this research. Studies had found that the most appropriate time to segment abandoned lands was in September, according to the SAM and ML algorithms. During this period, the intensity of the green colour was the highest and the colour brightness of abandoned lands differed from the colour intensity of other lands

Application of an airborne hyper-spectral survey system CASI/SASI in the gold-silver-lead-zinc ore district of Huaniushan, Gansu, China

The airborne hyper-spectral survey system CASI/SASI, which has an integrated system for gathering both image an spectral data, is at the cutting edge developments in the remote-sensing field. It can be used to directly identify surface objects based on diagnostic spectral characteristics. In this paper, the CASI/SASI were used in the Huaniushan gold-silver-lead-zinc ore district–Gansu to produce a lithologic map, identify altered minerals, and map the mineralized-alteration zones. Radiometric correction, radiometric calibration, atmospheric correction (spectral reconstruction), and geometric corrections were carried out in ENVI to pre-process the measured data. A FieldSpec ® Pro FR portable spectrometer was used to obtain the spectral signatures of all types of rock samples, ore deposits, and mineralized-alteration zones. We extracted and analyzed the spectral characteristics of typical alteration minerals. On the basis of hyper-spectral data, ground-spectral data processing, and comparative analysis of the measured image spectrum, we used the spectral-angle-mapping (SAM) and mixture-tuned matchedfiltering (MTMF) methods to perform hyperspectral-alteration mineral  mapping of wall rock and mineralized-alteration-zone hyperspectral identification. Hyperspectral- remote- sensing geological- classification maps were produced as well as distribution maps of all kinds of alteration minerals and mineralized-alteration zones. Based on geological comprehensive analysis and field investigations, the range of mineral alteration was proven to be the same as shown by the remote-sensing imagery. Indications are that airborne hyperspectral- remote-sensing -image CASI/SASI offer good application results and show a promising potential as a tool in geological investigations. The results will provide the basis for hyperspectral remote-sensing prospecting in the same or similar unexplored areas.