Spectral Mapping Method Research Articles

Exploration of ecological environment construction and development paths in towns and cities by applying time series data

Abstract This paper explores the interaction mechanism between urbanization and the ecological environment based on the unique correlation between urbanization and the ecological environment. BISE and Savizky-Golay algorithms are combined to establish the time series data, and for the characteristic curves of the EVI time response of the land cover, the spectral angle mapping method is combined with the minimum distance method to form the spectral angle-minimum distance classification method. The application of time series data involves calculating the time and spatial change of urban land cover and analyzing the evolution of urban ecological environment elements. Based on the temporal and spatial consistency test and land cover analysis of urban ecological environment construction, as well as the development of urbanization construction, relevant development paths are proposed. The comprehensive score of new urbanization in the four districts and one city of Hengyang City is concentrated towards 1.6, indicating that the level of economic development and the construction of new urbanization have reached coupling at different levels, respectively.

Spectral mapping of zeolite bearing paleolake deposits at Lake Tecopa, California and its implications for mapping zeolites on Mars

• Zeolitic tuff at paleolake Tecopa investigated using in-situ and orbital methods. • Reasons for limited orbital detections of zeolites in paleolake. • Importance of ground investigation to interpreting orbital data. While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases. :

Microphysical Perturbation Experiments and Ensemble Forecasts on Summertime Heavy Rainfall over Northern Taiwan

Abstract Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%–161%, 66%–161%, and 65%–149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatial-forecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values. Significance Statement Heavy rainfall from summer convections is stochastic in terms of intensity and location; therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models’ sensitivity to cloud microphysical parameterizations and the feasibility of application to ensemble forecast. Promising results were obtained from simulations of 10 real cases. The cloud microphysical ensemble approach may provide reasonable forecasts of heavy rainfall probability and convincing rainfall spatial distribution, particularly for more organized convection.

Screening chronic myeloid leukemia neutrophils using a novel 3-Dimensional Spectral Gradient Mapping algorithm on hyperspectral images

Background and objective Early diagnosis of chronic myeloid leukemia (CML) is important for effective treatment. The high spectral and spatial resolution of hyperspectral cellular or tissue images coupled with image analysis algorithms may provide avenues to detect and diagnose diseases early. Many algorithms have been used to analyze medical hyperspectral image data, each having their own strengths and short-comings. We present a novel 3-Dimensional Spectral Gradient Mapping (3-D SGM) method to analyze hyperspectral image cubes of CML versus healthy blood smears.Methods In the present study, we analyzed 13 hyperspectral image cubes of CML and healthy neutrophils. The 3-D SGM algorithm was compared to the conventional Windowed Spectral Angle Mapping (Windowed SAM) method. The 3-D SGM exploited the spectral information of the image cube together with the inter-band and inter-pixel data by extracting the 3-D gradient vector from each pixel. The Windowed SAM determined the similarity between the averaged window of a 2×2 training pixel group and the test pixel, in the multidimensional spectral angle.Results The specificity measure of 3-D SGM (97.7%) was superior to Windowed SAM (72.7%) at ruling out the presence of the disease, making it potentially ideal for screening patients. The positive likelihood ratio value of 3-D SGM (16.70) was superior in diagnosing the presence of the disease (i.e., positive test for CML) versus Windowed SAM (2.26). An accuracy value of 84.2% was achieved with 3-D SGM versus only 70.2% for Windowed SAM.Conclusion The new method is efficient and robust for analyzing hyperspectral images of CML versus healthy neutrophils. It has the potential to be developed into an inexpensive, minimally invasive method for screening CML, and could directly facilitate early diagnosis and treatment of the disease.

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.

Application of Remote Sensing Tools to Assess the Land Use and Land Cover Change in Coatzacoalcos, Veracruz, Mexico

Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative technology and tools that support their various decision making systems. This study provides the most recent LULC change analysis for the last six years (2015–2021) of Coatzacoalcos, Veracruz, Mexico, one of the most important petrochemical cities in the world and host of the ongoing Interoceanic Corridor project. The analysis was carried out using Landsat 8 Operational Land Imager (OLI) satellite images, ancillary data and ground-based surveys and the Normalized Difference Vegetation Index (NDVI) to identify and to ameliorate the discrimination between four main macro-classes and fourteen classes. The LULC classification was performed using the maximum likelihood classifier (MLC) to produce maps for each year, as it was found to be the best approach when compared to minimum distance (MDM) and spectral angle mapping (SAM) methods. The macro-classes were water, built-up, vegetation and bare soil, whereas the classes were an improved classification within those. Our study achieved both user accuracy (UA) and producer accuracy (PA) above 90% for the proposed macro-classes and classes. The average Kappa coefficient for macro-classes was 0.93, while for classes it was 0.96, both comparable to previous studies. The results from the LULC analysis show that residential, industry and commercial areas slowed down their growth throughout the study period. These changes were associated with socio-economical drivers such as insecurity and lack of economic investments. Groves and trees presented steady behaviors, with small increments during the five-year period. Swamps, on the other hand, significantly degraded, being about 2% of the study area in 2015 and 0.93% in 2021. Dunes and medium and high vegetation densities (∼80%) transitioned mostly to low vegetation densities. This behavior is associated with rainfall below the annual reference and increments of surface runoff due to the loss of vegetation cover. Lastly, the present study seeks to highlight the importance of remote sensing for a better understanding of the dynamics between human–nature interactions and to provide information to assist planners and decision-makers for more sustainable land development.

Spectroscopy of Gossans and associated lithology and its implication in geo exploration- A Case Study in the Parts of Rajpura-Dariba Group, Rajasthan, India

The gossan exposures are sparsely distributed in the Dariba area and for detection and mapping of gossan and its associated lithology, spectral signatures have been used. Spectral reflectance and absorption features in VNIR-SWIR (visible and near-infrared - shortwave infrared) domain provide the scope for delineating the mineralogy of rocks. In the study, a few spectral mapping methods like Spectral angle mapper, Spectral information divergence and Band ratio indices were applied to ASTER (Advanced Space borne Thermal Emission Reflection Radiometer) calibrated data for delineating the lithology. These mapping methods could delineate boundaries for gossan and its associated rocks of Dariba; but above out of all mapping methods, spectral angle mapper spatial map has been validated with base detailed geological map in quantitatively and accuracy much better than spectral information divergence indices. The band ratio can demarcate the boundaries for gossans in the 4/2 ratio map, in the same way for associated rocks of Carbonates were well delineated in the (7+9)/8 ratio map and Quartzite 4/6 ratio map. Finally, the overextended boundaries of Dariba gossans and associated units found in the above validated spatial map, can be confirmed with field evidence can be helpful to update the base map of Dariba

Surface Electromyography Signal Classification for the Detection of Temporomandibular Joint Disorder using Spectral Mapping Method

Temporomandibular joint Disorder (TMD) is with multifaceted and complex signs and symptoms which makes day to day activities of an individual uneasy. Electromyographic (EMG) processing of related muscles recordings could provide an early and immediate detection of TMD. To detect the TMD using surface electromyography (SEMG) of Masseter and Temporalis muscle with discrete wavelet transform (DWT) using spectral coding. To analyze the data, a new feature selection approach in the spectral domain is proposed. For statistical analyses, SPSS version 24 is employed. The results of the study revealed that the proposed approach was able to improve the accuracy of the classification by implementing a combination of DWT and the Support Vector Machine (SVM). The proposed method also exhibited a significant improvement in its performance in terms of its accuracy with 93%. In addition, the statistical analysis revealed that the model was able to improve the mean rank of the experimental and control group.

Analysis of Projection Effects in OSIRIS‐REx Spectral Mapping Methods: Recommended Protocols for Facet‐Based Mapping

AbstractWe searched for an optimized protocol for mapping observations from a point spectrometer onto a shape model composed of triangular facets, in the context of NASA's asteroid sample return mission, OSIRIS‐REx (Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer). Our study was conducted before the spacecraft arrived at the mission target asteroid (101955) Bennu, and we used observational sequence plans of the OSIRIS‐REx Visible and InfraRed Spectrometer (OVIRS). We explored six methods of mapping data to shape model facets, using three spatial resolutions. We attempted to boost map fidelity by increasing the observational coverage of the surface. We find that increasing shape model resolution improves mapping quality. However, once the shape model's mean facet edge length is smaller than two‐fifths of the diameter of the instrument's field of view (FOV), the increase in quality tapers off. The six mapping methods can be broken into two categories: facets that (1) select or (2) combine (average) data from observations. The quality differences between similar averaging methods (clipped average, weighted average, etc.) are insignificant. Selecting the nearest observation to a facet best preserves an enclosed outcrop's shape and signal, but averaging spots are more conservative against errors in photometric modeling. A completely enclosed outcrop border expands into the surrounding region by 0.8–1.5 radii of the instrument's FOV. Regions smaller than the instrument's FOV are present in resulting maps; however, their signal strength is reduced as a function of their size relative to the instrument FOV.

PhaseDCN: A Phase-Enhanced Dual-Path Dilated Convolutional Network for Single-Channel Speech Enhancement

Recent deep neural network (DNN) based single-channel speech enhancement methods have achieved remarkable results in the time-frequency (TF) magnitude domain. To further improve the quality and intelligibility of enhanced speech, the attention to phase enhancement is also increasing. In this paper, we propose a novel dilated convolutional network (DCN) model to simultaneously enhance the magnitude and phase of noisy speech. Unlike the direct complex spectral mapping methods, we take the complex spectrum of the signal as the main target and the ideal ratio mask (IRM) as the auxiliary target in a multi-target learning framework to achieve their complementary advantages. Firstly, a feature extraction module is introduced to achieve the fusion of local and long-term features. Two different targets are learned separately, but share the common feature extraction module, which is helpful to extract more general and suitable features. During the joint learning, the intermediate estimation of the IRM target in the auxiliary path, contributing as the attention gating factors, helps to distinguish the speech or non-speech components of the complex-valued signals in the main path. To leverage more fine-grained long-term contextual information, we introduce a multi-scale dilated convolution approach for feature encoding. Moreover, the proposed model is a causal system, which can fully meet the low latency requirements of real-time speech products. Experimental results show that, compared with other advanced systems, the proposed model not only has better speech denoising performance and phase estimation accuracy, but also generalizes better in the speaker, noise, and channel mismatch cases.

SEM-Based Automated Mineralogy and Its Application in Geo- and Material Sciences

Scanning electron microscopy based automated mineralogy (SEM-AM) is a combined analytical tool initially designed for the characterisation of ores and mineral processing products. Measurements begin with the collection of backscattered electron (BSE) images and their handling with image analysis software routines. Subsequently, energy dispersive X-ray spectra (EDS) are gained at selected points according to the BSE image adjustments. Classification of the sample EDS spectra against a list of approved reference EDS spectra completes the measurement. Different classification algorithms and four principal SEM-AM measurement routines for point counting modal analysis, particle analysis, sparse phase search and EDS spectral mapping are offered by the relevant software providers. Application of SEM-AM requires a high-quality preparation of samples. Suitable non-evaporating and electron-beam stable epoxy resin mixtures and polishing of relief-free surfaces in particles and materials with very different hardness are the main challenges. As demonstrated by case examples in this contribution, the EDS spectral mapping methods appear to have the most promising potential for novel applications in metamorphic, igneous and sedimentary petrology, ore fingerprinting, ash particle analysis, characterisation of slags, forensic sciences, archaeometry and investigations of stoneware and ceramics. SEM-AM allows the quantification of the sizes, geometries and liberation of particles with different chemical compositions within a bulk sample and without previous phase separations. In addition, a virtual filtering of bulk particle samples by application of numerous filter criteria is possible. For a complete mineral phase identification, X-ray diffraction data should accompany the EDS chemical analysis. Many of the materials which potentially could be characterised by SEM-AM consist of amorphous and glassy phases. In such cases, the generic labelling of reference EDS spectra and their subsequent target component grouping allow SEM-AM for interesting and novel studies on many kinds of solid and particulate matter which are not feasible by other analytical methods.

Distributed High Temperature Monitoring of SMF under Electrical Arc Discharges Based on OFDR.

The distributed high temperature measurement of an optical fiber subjected to electric arc discharges based on optical frequency-domain reflectometry is experimentally demonstrated. The distributed temperature profile is attained in an open glow regime of a few milliamps with maximum detectable temperature up to 2100 ± 20 °C. The discharge arc-induced softened length of the fiber and mechanical stress are measured and statistically analyzed in terms of the correlation of the Rayleigh spectra. The large wavelength scanning range of OFDR enables much higher accuracy for the delay time measurement with a minimum measured delay of 40 fs. The delay shift over the entire heating range for a single discharge duration is statistically calculated by using a temporal correlation method. The reliability of the thermal sensitivity coefficient as 10 pm/°C for telecom single mode fiber (SMF, @1550 nm) is quantitatively analyzed and evaluated by the correlation coefficient. Lastly, a spectral mapping method is employed in spectrum monitoring for discharge dynamic impact on the optical path length (OPL) and local Rayleigh scatter.

Application of spectral mapping method to Dirac operator

Application of spectral mapping method to Dirac operator

Identification of iron-bearing minerals based on HySpex hyperspectral remote sensing data

Our research group built a super-low altitude detection platform with power delta wings and mounted it with a HySpex hyperspectral sensor. This platform has great potential for extracting alteration information as it can provide high spectral and spatial resolution images. The aim of this study is to verify the reliability and accuracy of alteration information extraction using this platform. The seven-scene hyperspectral images of the Karatagh region in Hami of Xinjiang obtained by the platform in September 2017 were used to identify iron-bearing minerals. First, these images were preprocessed by splicing, atmospheric correction, and geometric correction. Second, the images were compared with the United States Geological Survey (USGS) standard spectra of similarities between the end-member spectra of goethite-, limonite-, and jarosite-altered minerals. Third, the three altered minerals were mapped in the test area based on the end-member spectrum. The results show that the correlations between the end-member spectra of three altered minerals, including goethite, limonite, and jarosite, obtained from the HySpex image and corresponding spectra in USGS reach 0.92873, 0.95098, and 0.8875, respectively. The end-member spectral reflectance values were higher than those in the spectral library. The original spectral data after the continuum-removal and the local quantitative display show that the spectrum at the feature absorption location was similar to the reference spectrum. On this basis, the distribution maps of goethite, limonite, and jarosite iron-stained altered minerals in the Hami Gobi area were produced and field verified. The data show that the information of identified minerals from the HySpex hyperspectral data were in accordance with the actual situation. Both methods can recognize jarosite well, the spectral angular mapping method is better than the back propagation (BP) neural network for goethite recognition, and the BP neural network of limonite recognition is better than the spectral angular mapping method. The above results prove that the alteration information can be effectively identified using the HySpex hyperspectral data integrated on a super-low altitude detection platform, which is of great significance to the rapid delineation of minerogenic prospects area.

Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India

ABSTRACT The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAMtan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAMtan. The RSDPW and CM illustrate that the SIDSAMtan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (ҡ) of SAM, SID, and SIDSAMtan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (ҡ), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.

Spectral mapping method based on intervals of comonotonicity for modelling of radiative transfer in non-uniform gaseous media

ABSTRACT The aim of this work is to describe a Spectral Mapping Method (SMM) to split spectral intervals into smaller sets of wavenumbers, called intervals of comonotonicity, over which gas spectra in distinct states are rigorously linked through a strictly increasing function. Over small intervals of comonotonicity, the proposed method becomes, in theory, exact. The step-by-step process of construction of intervals of comonotonicity is described and explained. The present work focuses on the two-cell problem. Despite its full generality for the treatment of the blurring effect in k-distribution approaches, the strength of the method is illustrated: 1/ with a high number of subintervals on an IR signature configuration, widely recognized as among the most challenging in band model theory; 2/ with only two subintervals, in a highly non-uniform two-cell configuration. Comparisons with reference Line-By-Line calculations and a mapping technique founded on a scaled map illustrate its relevance for radiative heat transfer and spectroscopic applications.

Spectral mapping of thermal transport across SiC-water interfaces

In this investigation, the thermal transport across 3C-type silicon carbide (SiC) and water interfaces was analyzed by means of nonequilibrium molecular dynamics (NEMD) simulations. To understand the details of the mechanisms involved in the transport of energy across hard-soft interfaces, spectral mapping methods were implemented. It was observed that the phonon density of states (DOS) at the interface is affected by the atomic surface termination, crystallographic plane, and the wetting conditions for both phases, indicating that different vibrational modes contribute to the interfacial heat transfer process based on the interface configuration. Low-frequency modes were found to contribute the most for the C- and Si-terminated SiC(1 0 0) planes and the C-terminated SiC(1 1 1) plane, while a noticeable contribution from high-frequency modes was observed for the Si-terminated SiC(1 1 1) plane. Out-of-plane modes significantly contributed to the heat transfer in all the analyzed surfaces, while the heat flux composition by in-plane modes was notably smaller, particularly for the SiC(1 1 1) plane. The in-plane modes lower contribution to the interfacial heat flux was related to the interfacial bonding strength and liquid structuring formed at the interface. An agreement was found between the thermal boundary conductance dependence on the DOS overlap and the interfacial liquid structure, while the interfacial bonding strength did not conclusively inform on the thermal transport behavior across these interfaces.

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.

Su Kalitesinin Sınıflandırılmasında Spektral Sınıflandırma Yöntemlerinin Karşılaştırılması

Today, water quality and water pollution can be detected using remote sensing and its terrestrial components. Remote sensing does not only provide a quick solution to detect water quality and pollution, but it could also be low cost. Within the scope of the study, the water quality of the İmranlı area of the Kızılırmak River, one of the most important water resources of the Sivas region and the İmranlı dam on the river, was investigated by spectral classification methods. Water samples were taken from various points on the river and dam and their chemical oxygen demands were determined in the laboratory. In addition, the reflectance values of the water samples taken by the local spectral measurements were examined in order to use as end members for spectral classification. CHRIS Proba is used as satellite image. Match filtering (MF), spectral angle mapping (SAM) and spectral information divergence (SID) methods have been used as the spectral classification methods and it has been examined which method gives better results in determining water quality. According to the results, it is understood that SAM method provides better classification accuracy than other methods.

Deep convolutional networks with residual learning for accurate spectral-spatial denoising

Abstract Although hyperspectral image (HSI) denoising has been studied for decades, preserving spectral data efficiently remains an open problem. In this paper, we present a powerful and trainable spectral difference mapping method based on convolutional networks with residual learning in an end-to-end fashion for preserving spectral profile while removing noise in HSIs. Our approach, called SSDRN (spectral-spatial denoising by residual network), blends a spectral difference mapping strategy with a denoised key band for an efficient complete set of HSI denoising. The key band is selected based on a principal component transformation matrix. Experiments have been conducted on both ground based HSIs and airborne data. Comparative analyses validate that the proposed method presents superior denoising performance as it preserves spectral information better, and requires less computational time.