Color Consistency of UAV Imagery Using Multichannel CNN-Based Image-to-Image Regression and Residual Learning

Abstract

Remote sensing images often suffer from color distortion, which can pose significant challenges for accurate data interpretation. To overcome this issue, this study developed a new approach called the multi-channel convolutional neural network (mCNN). This technique treated color calibration of remote sensing images as an image-to-image regression problem and used a neural network to learn a mapping function between the distorted and calibrated images by assigning varying weights to pixels across the scene. The mCNN consisted of three convolutional layer groups, each applied to one image channel separately. The input to each group was a residual channel, which is the difference between the distorted and calibrated images for that channel. A fusion layer was added to concatenate the outputs of the last convolutional layer from all channels, and then applied a regression layer to generate a full output image while preserving its structural details. Unlike other deep learning methods that work in multiple steps, the mCNN performed color calibration in a single step, leading to more efficient processing of large datasets. The method was validated using high-quality multi-type ground-truth datasets and compared with other color correction methods as well as its variants. Results showed that the mCNN model outperformed all competitors in terms of the mean value of the estimation errors, with a margin of 34.24%. These findings suggest that the proposed method is highly effective in addressing color distortion issues in remote sensing images, which is critical for accurately representing vegetation properties.