{"id":6942,"date":"2022-04-06T18:27:36","date_gmt":"2022-04-06T10:27:36","guid":{"rendered":"https:\/\/www.uavfordrone.com\/?post_type=product&p=6942"},"modified":"2022-04-06T18:54:52","modified_gmt":"2022-04-06T10:54:52","slug":"hyperspectral-camera-sensor-for-dji-m300-drone-sdk-payload","status":"publish","type":"product","link":"https:\/\/www.uavfordrone.com\/product\/hyperspectral-camera-sensor-for-dji-m300-drone-sdk-payload\/","title":{"rendered":"Hyperspectral Camera Sensor for DJI M300 Drone SDK Payload"},"content":{"rendered":"
The latest upgrade – Mini3-VN hyperspectral camera, based on DJI Payload SDK, compatible with DJI Matrice 300, adopts holographic transmission grating spectroscopic line scanning system and built-in micro stabilization system and obtains the spectral distribution of each pixel while acquiring the image of the research object, quantitatively Analyze the biophysical and chemical processes and parameters of the earth’s surface for research on vegetation type classification, vegetation growth monitoring, etc., target recognition, camouflage and anti-camouflage military fields, ground object and water telemetry, modern precision agriculture and other ecological environment monitoring fields. No contact, no damage, large scale, fast and accurate features.<\/p>\n
Feature:<\/p>\n
Acquisition and processing software: SpecVIEW
\nAcquisition function: exposure, gain, speed can be flexibly set, and real-time hyperspectral images and hyperspectral curves can be dynamically displayed; with the auxiliary camera, what you see is what you get; with automatic exposure, automatic speed matching, automatic data saving, and other functions; support any Three-band composite real-time display, image return; support one-key collection of black and white frames and large-area calibration target data collection; data storage formats are widely used, with commonly used data review and correction functions, and optimized fast correction and special correction algorithms.<\/p>\n
Preprocessing functions: geometric correction, reflectivity correction, regional correction, radiometric correction, and other functions; cluster analysis, single-band, true and false color, more than 20 kinds of vegetation indices (can be customized) can be obtained with one key without third-party software, Image 3D cropping, target spectral recognition, and other images, all of the above functions can realize unattended batch processing. It can automatically use the GPS information and flight attitude data obtained by GPS and inertial navigation systems to perform automatic geometric correction of hyperspectral images, and can significantly eliminate image distortion caused by the motion of airborne platforms such as drones, and the processed images will not be distorted. There should be an obvious band dislocation phenomenon; it can effectively and automatically splice the corrected band data, and the splicing result does not contain an obvious dislocation phenomenon; it provides the function of radiometric calibration for all bands.<\/p>\n
Specs:<\/p>\n
Spectral range\uff1a<\/td>\n | 400-1000 nm<\/td>\n | Spectral Resolution\uff1a<\/td>\n | 5nm<\/td>\n<\/tr>\n |
Sampling spectral resolution:<\/td>\n | 2.5nm<\/td>\n | NUC:<\/td>\n | 7th generation i5, 8G memory, 256G SSD<\/td>\n<\/tr>\n |
Number of spectral channels:<\/td>\n | 224<\/td>\n | Number of spatial channels:<\/td>\n | 1024<\/td>\n<\/tr>\n |
Detector:<\/td>\n | Scientific grade CMOS<\/td>\n | Pixel pitch:<\/td>\n | 9.97\u03bcm<\/td>\n<\/tr>\n |
imaging speed:<\/td>\n | 4s\/cube<\/td>\n | Data output:<\/td>\n | 12 bit<\/td>\n<\/tr>\n |
Spatial resolution:<\/td>\n | 0.12m @300m high<\/td>\n | Connection method:<\/td>\n | Gige<\/td>\n<\/tr>\n |
Power:<\/td>\n | 45W<\/td>\n | FPV Camera resolution:<\/td>\n | 5MP<\/td>\n<\/tr>\n |
Numerical aperture:<\/td>\n | F\/1.7<\/td>\n | FOV:<\/td>\n | 23\u00b0<\/td>\n<\/tr>\n |
Imaging method:<\/td>\n | raster push-broom<\/td>\n | Weight:<\/td>\n | 1Kg<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n 1. Agriculture application; rapid identification and classification of crops.<\/strong> <\/p>\n 2. Analysis of soil water content and soil fertility. <\/p>\n 3. Tree species classification, monitoring tree height <\/p>\n The hyperspectral data of forests at different heights were photographed, and ENVI was used to perform statistical algorithm learning to evaluate changes in tree height during the monitoring period.<\/p>\n <\/p>\n |