Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery

Abstract

In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground.

Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions

“…Since capturing new ground-truth datasets is tedious, time-consuming, and costly, designing new approaches that effectively deal with limited amounts of labeled training data is a vital research area. Additionally, there are approaches, especially exploiting tensor-based techniques [57][58][59][60] and semi-supervised learning [50], that were proven highly effective and possible to train from small T's, and could be efficiently implemented in FPGAand GPU-based architectures. Our current research efforts are focused on understanding the robustness of CNNs against small training samples in the HSI analysis tasks [105], and will also include confronting classical machine learning and deep learning algorithms in such scenarios.…”

“…Among the solutions that can help deal with limited amount of ground-truth data, are the unsupervised [46] and semi-supervised [47,48] approaches, including active learning [49]. In [50], Protopapadakis et al utilized a very small portion of labeled examples (constituting less than 0.08% of the available data) to train their deep models. Additionally, a semi-supervised technique was used to process unlabeled data, and to estimate soft labels which are later exploited to improve the training process.…”

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

“…The application of semisupervised learning technology in satellite remote sensing land classification is still in the development stage. Experiments are only performed on some simple datasets and have not been applied to actual scenes on a large scale, such as image classification [75]- [79] and information extraction [80]- [83]. In large-scale scenarios, three assumptions of semisupervised learning cannot be satisfied if unlabeled samples are added blindly.…”

Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

“…In addition, they used an F-Beta measure to assist the method in accounting for skewed class distributions. Protopapadakis et al [41] extracted buildings from satellite images with near infrared band, based on a deep learning model called Stacked Autoencoders Driven (SAD) and Semi-Supervised Learning (SSL). To train the deep model, they used only a very small amount of labeled data.…”