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Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

The need to monitor the Earth’s surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together.

Accurate land use and land cover (LU/LC) classification is essential for various geospatial applications. This research applied a Spectral Angle Mapper (SAM) classifier on the Landsat 7 (ETM+ 2010) & 8 (OLI 2020) satellite scenes to identify the land cover materials of the Shatt al-Arab region which is located in the east of Basra province during ten years with an estimate of the spectral signature using ENVI 5.6 software of each cover with the proportion of its area to the area of the study region and produce maps of the classified region. The bands of these datasets were analyzed using the Optimum Index Factor (OIF) statistic. The highest OIF represents the best and most appropriate band combination calculated for the classification process are (SWIR_2, SWIR_1, Blue) and (SWIR_2, SWIR_1, coastal aerosol) bands combination at (100.236 & 104.154) for ETM+, and OLI datasets, respectively, which adopted to obtain the most accurate interpretation of the land cover. The Landsat 7 (ETM+ 2010) is selected as a reference year to study the change in land cover features through ten years for this region using the novel Scene Optimum Index Factor (SOIF), which was suggested in this research. The amount of change for vegetation cover was 34 %, using the SAM classifier. The urban class was the most stable, and the rate of change was 23 %. The most affected were the water bodies, where the rate of change reached 73% due to the region falling into the tails of rivers, as well as the lack of water discharges coming from neighbouring and upstream countries. The research provides important information about land cover changes over the past decade due to the precise spectral analyses, showing the need for monitoring natural resources, especially in environmentally sensitive areas such as water bodies and vegetation cover. Environmental conservation efforts and continuous planning in affected regions may be supported by these findings.

The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV.

Significance: Hyperspectral imaging (HSI) is an emerging optical technique that has a double function of spectroscopy and imaging. Aim: Near-infrared hyperspectral imaging (NIR-HSI) (900 to 1700 nm) with the help of chemometrics was investigated for gastric cancer diagnosis. Approach: Mean spectra and standard deviation of normal and cancerous pixels were extracted. Principal component analysis (PCA) was used to compress the dimension of hypercube data and select the optimal wavelengths. Moreover, spectral angle mapper (SAM) was utilized as chemometrics to discriminate gastric cancer from normal. Results: Major spectral difference of cancerous and normal gastric tissue was observed around 975, 1215, and 1450 nm by comparison. A total of six wavelengths (i.e., 975, 1075, 1215, 1275, 1390, and 1450 nm) were then selected as optimal wavelengths by PCA. The accuracy using SAM is up to 90% according to hematoxylin–eosin results. Conclusions: These results suggest that NIR-HSI has the potential as a cutting-edge optical diagnostic technique for gastric cancer diagnosis with suitable chemometrics.

Developments of anti-gametocyte drugs have been delayed due to insufficient understanding of gametocyte biology. We report a systematic workflow of data processing algorithms to quantify changes in the absorption spectrum and cell morphology of single malaria-infected erythrocytes. These changes may serve as biomarkers instrumental for the future development of antimalarial strategies, especially for anti-gametocyte drug design and testing. Image-based biomarkers may also be useful for nondestructive, label-free malaria detection and drug efficacy evaluation in resource-limited communities. We extend the application of hyperspectral microscopy to provide detailed insights into gametocyte stage progression through the quantitative analysis of absorbance spectra and cell morphology in malaria-infected erythrocytes. Malaria-infected erythrocytes at asexual and different gametocytogenesis stages were imaged through hyperspectral confocal microscopy. The preprocessing of the hyperspectral data cubes to transform them to color images and spectral angle mapper (SAM) analysis were first used to segment hemoglobin (Hb)- and hemozoin (Hz)-abundant areas within the host erythrocytes. Correlations between changes in cell morphology and increasing Hz-abundant areas of the infected erythrocytes were then examined to test their potential as optical biomarkers to determine the progression of infection, involving transitions from asexual to various gametocytogenesis stages. Following successful segmentation of Hb- and Hz-abundant areas in malaria-infected erythrocytes through SAM analysis, a modest correlation between the segmented Hz-abundant area and cell shape changes over time was observed. A significant increase in both the areal fraction of Hz and the ellipticity of the cell confirms that the Hz fraction change correlates with the progression of gametocytogenesis. Our workflow enables the quantification of changes in host cell morphology and the relative contents of Hb and Hz at various parasite growth stages. The quantified results exhibit a trend that both the segmented areal fraction of intracellular Hz and the ellipticity of the host cell increase as gametocytogenesis progresses, suggesting that these two metrics may serve as useful biomarkers to determine the stage of gametocytogenesis.

Landsat time series commonly contain missing observations, i.e., gaps, due to the orbit and sensing geometry, data acquisition strategy, and cloud contamination. A spectral-angle-mapper (SAM) based spatio-temporal similarity (SAMSTS) gap-filling algorithm is presented that is designed to fill small and large area gaps in Landsat data, using one year or less of data and without using other satellite data. Each gap pixel is filled by an alternative similar pixel that is located in a non-missing region of the image. The alternative similar pixel locations are identified by comparison of reflectance time series using a SAM metric revised to be adaptive to missing observations. A time series segmentation-and-clustering approach is used to increase the search efficiency. The SAMSTS algorithm is demonstrated using six months of Landsat 8 Operational Land Imager (OLI) reflectance time series over three 150 × 150 km (5000 × 5000 30 m pixels) areas in California, Minnesota and Kansas. The three areas contain different land cover types, especially crops that have different phenology and abrupt changes due to agricultural harvesting, which make gap filling challenging. Fillings on simulated gaps, which are equivalent to 36% of 5000 × 5000 images in each test area, are presented. The gap filling accuracy is assessed quantitatively, and the SAMSTS algorithm is shown to perform better than the simple closest temporal pixel substitution gap filling approach and the sinusoidal harmonic model-based gap filling approach. The SAMSTS algorithm provides gap-filled data with five-band reflective-wavelength root-mean-square differences less the 0.02, which is comparable to the OLI reflectance calibration accuracy.

Remote sensing applications for marine placer deposit exploration remain limited due to the mineralogical complexity and dynamic coastal processes. This study presents the first medium- to high-level detailed multi-scale remote sensing analysis of placer deposits in the Rías Baixas, NW Spain, focusing on five beaches within the Vigo Estuary. Ten beach samples were analyzed for their heavy mineral (HM) content and spectral signatures, using bromoform separation and FieldSpec 4 spectroradiometer equipment, respectively. The spectral signatures of beach samples with a high HM content were characterized and resampled for the Sentinel-2 application, employing the Spectral Angle Mapper (SAM) algorithm. Field validation and an unmanned aerial vehicle (UAV) survey confirmed surface placer occurrences and the SAM’s results. Santa Marta Beach exhibited significant placer anomalies (up to 30% HM), correlating with low SAM values (minimum value–0.10), indicating high spectral similarity. The SAM-derived anomaly patches aligned with the field observations, demonstrating Sentinel-2’s potential for placer deposit mapping. This work highlights the application of Sentinel-2 in the exploration of placer deposits and the use of a specific spectral range of these deposits in coastal environments. These tools are non-invasive, more environmentally friendly, and sustainable, and can be extrapolated to other regions of the world with similar characteristics.

Mal secco disease (MSD), caused by Plenodomus tracheiphilus, poses a serious threat to Citrus limon production across the Mediterranean Basin. This study investigates the potential of high-resolution WorldView-3 imagery for detecting early-stage MSD symptoms in lemon orchards through the integration of three pansharpening algorithms(Gram–Schmidt, NNDiffuse, and Brovey) with two machine learning classifiers (Random Forest and Support Vector Machine). The Brovey-based fusion combined with Random Forest yielded the best results, achieving 80% overall accuracy, 90% precision, and 84% recall, with high spatial reliability confirmed by 10-fold cross-validation. Spectral analysis revealed that Brovey introduced the largest radiometric deviation, particularly in the NIR band, which nonetheless enhanced class separability between healthy and symptomatic crowns. These findings demonstrate that moderate spectral distortion can be tolerated, or even beneficial, for vegetation disease detection. The proposed workflow—efficient, transferable, and based solely on visible and NIR bands—offers a practical foundation for satellite-driven disease monitoring and precision management in Mediterranean citrus systems.

Ore and waste discrimination is essential for optimizing exploitation and minimizing ore dilution in a mining operation. The conventional ore/waste discrimination approach relies on the interpretation of ore control by geologists, which is subjective, time-consuming, and can cause safety hazards. Hyperspectral remote sensing can be used as an alternative approach for ore/waste discrimination. The focus of this study is to investigate the application of hyperspectral remote sensing and deep learning (DL) for real-time ore and waste classification. Hyperspectral images of several meters of drill core samples from a silver ore deposit labeled by a site geologist as ore and waste material were used to train and test the models. A DL model was trained on the labels generated by a spectral angle mapper (SAM) machine learning technique. The performance on ore/waste discrimination of three classifiers (supervised DL and SAM, and unsupervised k-means clustering) was evaluated using Rand Error and Pixel Error as disagreement analysis and accuracy assessment indices. The results showed that the DL method outperformed the other two techniques. The performance of the DL model reached 0.89, 0.95, 0.89, and 0.91, respectively, on overall accuracy, precision, recall, and F1 score, which indicate the strong capability of the DL model in ore and waste discrimination. An integrated hyperspectral imaging and DL technique has strong potential to be used for practical and efficient discrimination of ore and waste in a near real-time manner.