Estimating Index of Refraction from Polarimetric Hyperspectral Imaging Measurements

Current material identification techniques rely on estimating reflectivity or emissivity which vary with viewing angle. As off-nadir remote sensing platforms become increasingly prevalent, techniques robust to changing viewing geometries are desired. A technique leveraging polarimetric hyperspectral imaging (P-HSI), to estimate complex index of refraction, N̂(ν̃), an inherent material property, is presented. The imaginary component of N̂(ν̃) is modeled using a small number of “knot” points and interpolation at in-between frequencies ν̃. The real component is derived via the Kramers-Kronig relationship. P-HSI measurements of blackbody radiation scattered off of a smooth quartz window show that N̂(ν̃) can be retrieved to within 0.08 RMS error between 875 cm−1 ≤ ν̃ ≤ 1250 cm−1. P-HSI emission measurements of a heated smooth Pyrex beaker also enable successful N̂(ν̃) estimates, which are also invariant to object temperature

NEW RECORDS OF TWO RARELY ENCOUNTERED, ENDEMIC RATS (RODENTIA: MURIDAE: MURINAE) FROM GUNUNG GANDANGDEWATA, WEST SULAWESI PROVINCE

We collected specimens of Sommer’s Sulawesi shrew-rat, Sommeromys macrorhinos, at three sites (1600, 2200, and 2600 m) and the Sulawesi small-bodied shrew-rat, Crunomys celebensis, at one site (1600 m) on Gunung Gandangdewata in the western block  of the central core of Sulawesi during November 2011 and May 2012. Prior to 2011, S. macrorhinos was known only from the holotype, which was taken on 2 August 1973 at 2400 m near the summit of Gunung Tokala (upper montane forest). Previously, C. celebensis was known only from tropical lowland evergreen rain forest in the Danau Lindu valley and nearby upper drainage of the Sungai Miu in the northern portion of the westcentral mountain block in Sulawesi’s central core. The new specimens of S. macrorhinos and C. celebensis extend their known range of habitats to include the transition between lowland and montane forest. Because the original description of S. macrorhinos was based on a single specimen, we describe some external morphological features and provide measurements of new specimens as a supplement to the original description.Key words: Crunomys celebensis, morphology, shrew-rat, Sommeromys macrorhino

西村先生講話 第二回

Nexus file containing phased alleles from BDNF sequences of Sunda shelf Crocidura. This alignment was used to generate the gene trees in Figures S1 and S

Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an auto- mated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene

Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models

Body-size measurement errors are usually ignored in stock assessments, but may be important when body-size data (e.g., from visual sur veys) are imprecise. We used experiments and models to quantify measurement errors and their effects on assessment models for sea scallops (Placopecten magellanicus). Errors in size data obscured modes from strong year classes and increased frequency and size of the largest and smallest sizes, potentially biasing growth, mortality, and biomass estimates. Modeling techniques for errors in age data proved useful for errors in size data. In terms of a goodness of model fit to the assessment data, it was more important to accommodate variance than bias. Models that accommodated size errors fitted size data substantially better. We recommend experimental quantification of errors along with a modeling approach that accommodates measurement errors because a direct algebraic approach was not robust and because error parameters were diff icult to estimate in our assessment model. The importance of measurement errors depends on many factors and should be evaluated on a case by case basis