Ambiguity of Underwater Color Measurement and Color-based Habitat Classification

The paper discusses ambiguities in recording color underwater. Routinely collected RGB imagery can be used for classification and recognition utilizing the proposed probabilistic approach. The device for collection of spectral signatures, necessary for this approach is described

Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

<p>Abstract</p> <p>Background</p> <p>We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis.</p> <p>Results</p> <p>For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains.</p> <p>Conclusion</p> <p>Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery.</p

Long-range UAV Thermal Geo-localization with Satellite Imagery

Onboard sensors, such as cameras and thermal sensors, have emerged as effective alternatives to Global Positioning System (GPS) for geo-localization in Unmanned Aerial Vehicle (UAV) navigation. Since GPS can suffer from signal loss and spoofing problems, researchers have explored camera-based techniques such as Visual Geo-localization (VG) using satellite RGB imagery. Additionally, thermal geo-localization (TG) has become crucial for long-range UAV flights in low-illumination environments. This paper proposes a novel thermal geo-localization framework using satellite RGB imagery, which includes multiple domain adaptation methods to address the limited availability of paired thermal and satellite images. The experimental results demonstrate the effectiveness of the proposed approach in achieving reliable thermal geo-localization performance, even in thermal images with indistinct self-similar features. We evaluate our approach on real data collected onboard a UAV. We also release the code and \textit{Boson-nighttime}, a dataset of paired satellite-thermal and unpaired satellite images for thermal geo-localization with satellite imagery. To the best of our knowledge, this work is the first to propose a thermal geo-localization method using satellite RGB imagery in long-range flights.Comment: 8 pages, 6 figures, IROS 202

Transition and Evaluation of RGB Imagery to WFOs and National Centers by NASA SPoRT

MODIS Snow/Cloud and True Color RGB imagery has been used by SPoRT partners since 2004 to examine changes in surface features such as snow cover, vegetation, ocean color, fires, smoke plumes, and oil spills

Novel Methods for RGB Aerial Image Analysis

Multiple linear regression models were developed to predict sand and clay content along with soil organic matter content from RGB imagery from both commercially available satellite imagery as well as RGB UAV imagery. UAV Imagery was tested at two flight altitudes to determine if lower or higher altitude had an effect on prediction. In cases of sand, clay, and OM content, flight altitudes did not significantly differ in prediction abilities. Satellite imagery was evaluated using data from Planet Labs as well as Google Earth. Regression models were developed to predict sand, clay, and soil organic matter content from these satellite images, which captured fields with bare soil. An alternative to whole field data collection, referred to herein as the point sampling method, was introduced. A survey of currently available neural network and machine learning technologies was performed to establish which of these technologies could benefit the precision agriculture industry. A sample model was trained to detect and classify cotton blooms from low-altitude RGB imagery collected from a DJI Phantom 3 UAV