Giwoong Lee

Remote sensing images are often degraded by atmospheric effects, low illumination, and off-nadir viewing, which reduces the segmentation performance of deep models. KOMPSAT (Korea Multi-Purpose Satellite) imagery suffers from quality degradation because the Korean Peninsula is surrounded by sea on three sides and is subject to frequent weather and atmospheric variations. In practice, operators apply heuristic image enhancement techniques by hand, but these approaches are labor-intensive and inconsistent. To address this issue, we have proposed Adaptive Remote Sensing Image Enhancement (ARSIE), an automated reinforcement learning–based framework that improves segmentation performance on degraded KOMPSAT imagery. ARSIE takes only an existing segmentation network and training data as input, and learns, for each image, a sequence of enhancement operations selected from a filter pool. The policy network uses intermediate feature maps from the segmentation model to choose the next operation, ensuring that enhancement decisions directly support downstream segmentation performance. Experimental results show that ARSIE automatically discovers image-specific enhancement combinations and consistently improves segmentation accuracy on degraded KOMPSAT imagery. We demonstrate that ARSIE has the potential to be extended to improving the quality of other satellite imagery.

We propose HYBOOD, a hybrid out-of-distribution model based on normalizing flow followed by a simple linear classification model. In real-world settings, it is known that data corruption has a strong influence on model degradation; for example image quality like noise, blur and image geometry like translation, scaling, rotation. MNIST-C, CIFAR10-C are the general synthesized datasets to measure model performance and corruption difficulty in terms of covariate and semantic shifts. HYBOOD shows that the separability between in-distribution, covariate shift, and semantic shift can be represented by distribution distance and log-scale density. We also find out the attributes of covariate shifts are ordered by corruption difficulty ranking (CDR) for the datasets. To the best of our knowledge, this is the first method to measure data corruption difficulty with generative models using Wasserstein Distance, Mutual Information and Minimal Description Length. In this paper, we pose interesting experimental results that the MNIST-C trained generative model is most deteriorated by fog, impulse noise and stripe corruption types. This can be interpreted that those attributes are challenging corruptions to the generative model in uncertainty and complexity. By training in-distribution data only, HYBOOD achieves out-of-distribution detection performance for distinguishable covariate and semantic shifts, and quantifying covariate shift ranking.