Robust Semisupervised Land-Use Classification Using Remote Sensing Data With Weak Labels

Abstract

This work develops robust semisupervised classifiers to tackle the three most challenging problems in land-use classification using remote sensing data, namely, information imbalance, label noise, and image uncertainty. Limited by technology and cost, collecting clean labels for remote sensing images is difficult and often impractical. The change of environment and time also increases the uncertainty of remote sensing images. To overcome the obstacles incurred by the mixed pixels and weak labels, this work proposes dividing the pixels in remote sensing images into two groups, namely, pixels with accurate labels and those with weak labels, before processing the weakly labeled pixels using a nuclear norm-based cost function. To address the imbalanced data problem in pixels with accurate labels, an improved cross-entropybased cost function is proposed to weigh the contributions from data of different classes based on their importance by exploiting the term frequency-inverse document frequency (TF-IDF) algorithm. Finally, an artificial class called "unknown" is proposed to cope with the interference caused by weakly labeled data with unrepresentative spatial features. Extensive experiments validate the effectiveness of the proposed semisupervised classifier.

Effect of Bit Depth on Cloud Segmentation of Remote-Sensing Images

“…Existing studies show that the mean annual cloud cover of global remote-sensing images is about 66% [3]. The presence of clouds hinders the application of remote-sensing images in Earth observation [4,5]. Therefore, accurately detecting clouds on remote-sensing images has become a prominent issue.…”