Abstract: A level of detail-transformation adaptive enhancement method for image contrast includes: dividing a remote sensing image into a plurality images of different levels of detail, the lowest level of detail defined as L and the highest level of detail defined as H, and gradually transforming an image Imagei of an arbitrary level of detail i between the image ImageH of the highest level of detail H and the image ImageL of the lowest level of detail L from ImageL to ImageH through the following equation: Imagei=Ri×ImageH+(1?Ri)×ImageL. The image ImageH of the highest level of detail H is an image ImageACE produced with adaptive contrast enhancement processing, or an image produced with a contrast enhancement method such as Gaussian or histogram equalization; the image ImageL of the lowest level of detail L is an image ImageLCE produced by common linear contrast enhancement.

Abstract: A level of detail-transformation adaptive enhancement method for image contrast includes: dividing a remote sensing image into a plurality images of different levels of detail, the lowest level of detail defined as L and the highest level of detail defined as H, and gradually transforming an image Imagei of an arbitrary level of detail i between the image ImageH of the highest level of detail H and the image ImageL of the lowest level of detail L from ImageL to ImageH through the following equation: Imagei=Ri×ImageH+(1?Ri)×ImageL. The image ImageH of the highest level of detail H is an image ImageACE produced with adaptive contrast enhancement processing, or an image produced with a contrast enhancement method such as Gaussian or histogram equalization; the image ImageL of the lowest level of detail L is an image ImageLCE produced by common linear contrast enhancement.

Abstract: An unmanned aerial vehicle image processing system includes a digital camera mounted on an unmanned aerial vehicle, an autopilot controller installed in the unmanned aerial vehicle and pre-loaded with a base image, and a computer device. The autopilot controller programs a flight path as a series of mission lags to cover a target polygon on the base image in a lawnmower pattern for controlling the unmanned aerial vehicle to perform a flight mission in which the autopilot controller records plural GPS/INS data and triggers the digital camera to take plural photos which are further calibrated to generate plural calibrated photos corresponding to the plural GPS/INS data, respectively. The computer device processes the plural calibrated photos to generate plural projected photos by using the corresponding plural GPS/INS data, respectively, and stitches the plural projected photos to a mosaic image by a photo-stitching process.

Abstract: An unmanned aerial vehicle image processing system includes a digital camera mounted on an unmanned aerial vehicle, an autopilot controller installed in the unmanned aerial vehicle and pre-loaded with a base image, and a computer device. The autopilot controller programs a flight path as a series of mission lags to cover a target polygon on the base image in a lawnmower pattern for controlling the unmanned aerial vehicle to perform a flight mission in which the autopilot controller records plural GPS/INS data and triggers the digital camera to take plural photos which are further calibrated to generate plural calibrated photos corresponding to the plural GPS/INS data, respectively. The computer device processes the plural calibrated photos to generate plural projected photos by using the corresponding plural GPS/INS data, respectively, and stitches the plural projected photos to a mosaic image by a photo-stitching process.