Embodiments include a method for retrieving atmospheric aerosol based on statistical segmentation. Firstly a multi-band remote sensing image including an apparent reflectance and an aerosol optical thickness look-up table corresponding to a retrieval band is obtained, then pixels are partitioned and screened according to apparent reflectance segments of a mid-infrared 2.1 micrometer band. After that the retained pixel sets are further partitioned and screened according to the apparent reflectance segments of the mid-infrared 1.6 micrometer band. Finally the obtained pixel sets are partitioned into two categories according to the pixel number, one category including pixels having more pixels, the other including those with less pixels. The category with more pixels is taken as the reference part for retrieval.

Various embodiments disclosed herein describe a divided-aperture infrared spectral imaging (DAISI) system that is adapted to acquire multiple IR images of a scene with a single-shot (also referred to as a snapshot). The plurality of acquired images having different wavelength compositions that are obtained generally simultaneously. The system includes at least two optical channels that are spatially and spectrally different from one another. Each of the at least two optical channels are configured to transfer IR radiation incident on the optical system towards an optical FPA unit comprising at least two detector arrays disposed in the focal plane of two corresponding focusing lenses. The system further comprises at least one temperature reference source or surface that is used to dynamically calibrate the two detector arrays and compensate for a temperature difference between the two detector arrays.

A power station inspection system and a power station inspection method are disclosed. A power station inspection system includes an unmanned aerial vehicle, and an unmanned aerial vehicle flight controller, a central controller, and at least two types of data collection terminals that are disposed on the unmanned aerial vehicle. The central controller is configured to control the unmanned aerial vehicle flight controller, so that the unmanned aerial vehicle flight controller controls flight of the unmanned aerial vehicle. The central controller is further configured to control the at least two types of data collection terminals to collect at least two types of component image data of a power station component in a flight process of the unmanned aerial vehicle.

A method for monitoring the airspace around an aircraft or the road, runway, taxiway, movement area condition or any other object or surface of interest ahead of a vehicle, even in inclement weather is disclosed. The present teachings provide a system for characterizing cloud drops, cloud ice particles, other hydrometeors such drizzle, rain or falling snow, and for distinguishing dry surfaces from those covered by water, snow, frost, and various types of ice even when they cover only a fraction of the field of view of the road condition monitoring system, even in inclement weather.

Measurement apparatuses and methods are disclosed for generating high-precision and -accuracy gas concentration maps that can be overlaid with 3D topographic images by rapidly scanning one or several modulated laser beams with a spatially-encoded transmitter over a scene to build-up imagery. Independent measurements of the topographic target distance and path-integrated gas concentration are combined to yield a map of the path-averaged concentration between the sensor and each point in the image. This type of image is particularly useful for finding localized regions of elevated (or anomalous) gas concentration making it ideal for large-area leak detection and quantification applications including: oil and gas pipeline monitoring, chemical processing facility monitoring, and environmental monitoring.

A method for building and using soil models that determine soil properties from soil spectrum data is provided. In an embodiment, building soil model may be accomplished using soil spectrum data received via hyperspectral sensors from a land unit. A processor updates the soil spectrum data by removing interference signals from the soil spectrum data. Multiple ground sampling locations within the land unit are then determined based on the updated soil spectrum data. Soil property data are obtained from ground sampling at the ground sampling locations. Soil models that correlate the updated soil spectrum data with the soil property data are created based on the updated soil spectrum data and the soil property data. The soil models are sent to a storage for future use.

A gas detection device according to an embodiment includes a first irradiation part configured to irradiate a first light having a first wavelength on a gas released into a space, a second irradiation part configured to irradiate, on the gas, a second light having a second wavelength that is less than the first wavelength, an irradiation position adjustment part configured to control an irradiation position of the first light on the gas, a gas analysis part configured to analyze a component of the gas based on the first light having passed through the gas, and a gas visualization part configured to visualize a concentration distribution of the gas based on the second light having passed through the gas. The irradiation position adjustment part controls the irradiation position of the first light on the gas based on the visualized concentration distribution of the gas.

A method for determining national crop yields during the growing season is provided. In an embodiment, a server computer system receives agricultural data records for a particular year that represent covariate data values related to plants at a specific geo-location at a specific time. The system aggregates the records to create geo-specific time series for a geo-location over a specified time. The system creates aggregated time series from a subset of the geo-specific time series. The system selects a representative feature from the aggregated time series and creates a covariate matrix for each specific geographic area in computer memory. The system determines a specific crop yield for a specific year using linear regression to calculate the specific crop yield from the covariate matrix. The system determines a forecasted crop yield for the specific year using a sum of the specific crop yields for the specific year, as adjusted.

Embodiments include a method for retrieving atmospheric aerosol based on statistical segmentation. Firstly a multi-band remote sensing image including an apparent reflectance and an aerosol optical thickness look-up table corresponding to a retrieval band is obtained, then pixels are partitioned and screened according to apparent reflectance segments of a mid-infrared 2.1 micrometer band. After that the retained pixel sets are further partitioned and screened according to the apparent reflectance segments of the mid-infrared 1.6 micrometer band. Finally the obtained pixel sets are partitioned into two categories according to the pixel number, one category including pixels having more pixels, the other including those with less pixels. The category with more pixels is taken as the reference part for retrieval.

The present technology relates to a sensing system, a sensing method, and a sensing device which are capable of performing measurement with higher accuracy. A sensing system is configured such that a plurality of reference reflection regions having a reflectance corresponding to an inspection target are prepared for each wavelength band which is a target for sensing of the inspection target as reference reflection regions, and is configured to sense the reference reflection region having a reflectance corresponding to the inspection target for each wavelength band which is a target for sensing of the inspection target at the time of sensing a region including the inspection target and the reference reflection region. The present technology can be applied to a system for measuring a vegetation index such as a normalized difference vegetation index (NDVI).