Techniques are described for automated operations to determine tree inventory information for an area of land using visual data of overhead image(s), such as by using a trained prediction model specific to an ecological region to which the land area belongs as part of probabilistically determining multiple types of information about trees in that land area, and for subsequently using the determined tree inventory information in one or more manners (e.g., to improve management of trees in that land area). The images may, for example, include spectral satellite images that include at least visible light data for an area of land, and the determined tree inventory information may include information about the trees present on the land area, such as, for example, predictions of particular tree species, quantities of each of the tree species, sizes of the trees, etc.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

A method and device for restoring and reconstructing a light source spectrum based on a hyperspectral image are provided. A hyperspectral image is processed to obtain a first maximum spectrum. The first maximum spectrum is matched with a light source spectral basis vector set in a light source spectrum dictionary, and a linear decomposition is performed to obtain a dictionary set. The maximum value of each waveband is obtained and iterative approximation is performed. Therefore, it can quickly restore the spectrum of the light source of the shooting environment from a single hyperspectral image using a relatively simple calculation process, and can still achieve a good restoration effect for monochromatic image scenes or image scenes with few colors, even close to the real light source spectrum. After the light source spectrum is obtained, this information can be further utilized for different kinds of applications.

Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Disclosed a crop yield prediction method and system based on low-altitude remote sensing information from an unmanned aerial vehicle (UAV). Obtaining a plurality of images taken by the UAV; stitching the plurality of images to obtain a stitched image; performing spectral calibration on the stitched image to obtain the reflectivity of each pixel in the stitched image; using a threshold segmentation method to segment the stitched image, to obtain a target area for crop yield prediction; using a Pearson correlation analysis method to analyze a correlation between the reflectivity of each band and the growth status and yield of the crop to obtain feature bands; constructing yield prediction factors based on the feature bands; and determining a predicted crop yield value of the target area for crop yield prediction based on the yield prediction factors and a crop planting area of the target area for crop yield prediction.

Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Display device for displaying sub-surface structures including acquisition apparatus to acquire images of at least part of the user's body or an object from acquisition signals defining a pre-determinable multispectral radiation band, display to make at least one image accessible to an operator in real time, a processor to coordinate the acquisition apparatus and display and extract, from the images, reference signals including first surface and/or sub-surface localization points defined by the part of the body or object, a database operationally connected to the processor including a plurality of models of sub-surface structures of the part of the body or object, each defining predetermined configurations of second localization points. The processor compares models with reference signals and selects one model having second localization points matching more with the first localization points, and the display makes accessible the model selected so the operator can see the sub-surface structure.

According to certain embodiments, a system for detection of anomalous cells, comprises a hyperspectral imaging system; a memory having executable instructions stored thereon; and a processor configured to execute the executable instructions to cause the system to: receive a patient hyperspectral image comprising a pixel spectral signature for each pixel of the received patient hyperspectral image; classify the patient hyperspectral image by a machine learning model trained to classify hyperspectral images based on pixel spectral signatures; and provide an indication that the patient hyperspectral image contains an anomalous cell type, responsive to the classifying.

A method and apparatus of a device for generating an above ground biomass density prediction model is described. In an exemplary embodiment, the device receives a first set of satellite and optionally environmental data for the target landmass. In addition, the device trains an above ground biomass density model using at least the satellite data and Light Detection and Ranging (LIDAR) data. Furthermore, the device applies the above ground biomass density model using a second set of satellite and environmental biomass to generate the ground biomass density map.

A system and method for assessing categorized pixels of satellite images associated with agriculture land parcel using an artificial intelligence (AI) model are provided. The method includes, (i) obtaining satellite images associated with agriculture land parcel; (ii) pre-processing the satellite images to generate pre-processed satellite images, (iii) training the AI model by categorizing historical plurality of pixels from historical plurality of satellite images based on historical satellite data and correlating historical scores to historical categorized pixels to obtain trained AI model, (iv) classifying pixels of pre-processed satellite images into crop area-pixels and non-crop area pixels by determining a profile of time series data that corresponds to at least one of normalized difference vegetation index, normalized difference water index, land surface temperature, modified normalized difference water index, or land surface water index, (v) determining, using trained AI model, categorized pixels based on classification, (vi) assessing categorized pixels with a score.