Certain embodiments provide an imaging method for a non-line-of-sight object and an electronic device. In certain embodiments, the method includes: detecting a first input operation; and generating first image data in response to the first input operation. The first image data is imaging data of the non-line-of-sight object obtained by fusing second image data and third image data. The first image data includes position information between the non-line-of-sight object and a line-of-sight object. The second image data is imaging data of the line-of-sight object captured by the optical camera. The third image data is imaging data of the non-line-of-sight object captured by the electromagnetic sensor.

Certain embodiments provide an imaging method for a non-line-of-sight object and an electronic device. In certain embodiments, the method includes: detecting a first input operation; and generating first image data in response to the first input operation. The first image data is imaging data of the non-line-of-sight object obtained by fusing second image data and third image data. The first image data includes position information between the non-line-of-sight object and a line-of-sight object. The second image data is imaging data of the line-of-sight object captured by the optical camera. The third image data is imaging data of the non-line-of-sight object captured by the electromagnetic sensor.

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

In some examples, a retroreflective article may include a retroreflective substrate, and an optical pattern embodied on the retroreflective substrate. The optical pattern may include a first optical sub-pattern and a second optical sub-pattern, wherein the optical pattern represents a set of information that is interpretable based on a combination of the first optical sub-pattern that is visible in a first light spectrum and the second optical sub-pattern that is visible in a second light spectrum. The first and second light spectra may be different.

A method and apparatus for determining gaze direction information, includes a light source for forming illuminating light to an eye region of a user, and optical element(s) configured to guide the illuminating light from the light source to the eye region. The illuminating light is dynamically adjustable to generate a dynamic light beam on the eye region, and a sensor is configured to capture reflected light on the eye region and generate reflection eye data. The apparatus can maintain user profile information, adjust spectral power distribution of the light source based on the user profile information, receive the reflection eye data, and generate the gaze direction information based on the reflection eye data.

Provided are an image processing apparatus, an image processing method, and an image processing program capable of achieving high accuracy in an index representing vegetation. An image processing apparatus (1) includes a normal map generation unit (12) and a reflection characteristic model generation unit (18). The normal map generation unit (12) obtains a normal vector characteristic based on a polarized image acquired. The reflection characteristic model generation unit (18) estimates a reflection characteristic model based on the normal vector characteristic obtained by the normal map generation unit (12).

In an approach for identifying a crop using satellite observation data and crop rotation data, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region from an optical satellite. A processor selects a plurality of points from the aerial image. A processor clips the aerial image into one or more smaller images to generate one or more observed signatures. A processor determines there is a change in a spatial distribution of one or more crops. A processor generates an actual signature from the one or more observed signatures. A processor cross-correlates the actual signature against a plurality of historical reference signatures to determine a degree of similarity. A processor ranks one or more results of the cross-correlation. A processor identifies the one or more crops. A processor calculates a first estimate of an amount of the one or more crops.

A biological tissue analyzing device configured to analyze a biological tissue using hyperspectral data in which spectral information is associated with each of pixels forming a two-dimensional image and comprising the following (i) and (ii), as well as comprising (iii) and/or (iv): (i) a hyperspectral data acquisition unit configured to acquire the hyperspectral data; (ii) an analysis target region extraction unit configured to extract pixels corresponding to an analysis target region from a two-dimensional image of the biological tissue; (iii) an altered state classification unit configured to roughly classify an altered state of the biological tissue with unsupervised learning; and (iv) an altered state identification unit configured to identify the altered state of the biological tissue with supervised learning.

This invention discloses a practical method for landslide detection in large space, which comprises the following steps: image synthesis, ice and snow detection, removal of non-potential landslide area, detection of potential landslide area, feature calculation, landslide detection model construction and precision validation; this invention avoids radiometric correction and outlier by detecting landslide from synthetic image. That guarantees practical applicability of the proposal. Firstly, detecting potential landslides can avoid the imbalanced sample distribution issue between background objects and landslides when training the landslide detection model. The landslide is further detected by building a random forest model based on the spectral features and textural features of potential landslide pixels in different neighboring time domains. It fully considers the changes of objects in different time domains, and lays a foundation for efficient landslide extraction. This model is relatively reliable and practical for automatically detecting landslide from large-scale images.

The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.