Assessing optical remote sensing for grave detection.

The application of an effective and ready-to-use tool for discovering clandestine graves is crucial for solving a number of cases where disappearance of people is involved. This is the case in Mexico, where the government drug war has resulted in a large number of missing people that has been estimated to be over 40,000 since the year 2006. In this article, we report results from an experimental study on simulated animal graves detection using several techniques from optical remote sensing. Results showed that several spectral indices from hyperspectral and/or multispectral sensors may be used to detect N-enriched vegetation. Thermal imagery was also effective to detect underground voids through differential thermography, although this was only effective for detecting large graves with bare terrain. Lastly, while dense pointclouds reconstructed from oblique aerial photography was able to detect vegetation regrowth over the pits, the terrain subsidence was not sufficiently large to be detected with this technique, even in the case of mechanical removal of vegetation.

Prediction of dry matter content of recently harvested 'Hass' avocado fruits using hyperspectral imaging.

BACKGROUND: 'Hass' avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non-destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400-1000 nm. RESULTS: No correlation between fruit weight and color with the percentage of DM was found in the study area. Cross-validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross-validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R2 = 0.9, a root-mean-square error of 2.6 g kg-1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2. CONCLUSIONS: The results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry.

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study.

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.