Height estimation from single aerial images using a deep ordinal regression network

Understanding the 3D geometric structure of the Earth's surface has been an active research topic in photogrammetry and remote sensing community for decades, serving as an essential building block for various applications such as 3D digital city modeling, change detection, and city management. Previous researches have extensively studied the problem of height estimation from aerial images based on stereo or multi-view image matching. These methods require two or more images from different perspectives to reconstruct 3D coordinates with camera information provided. In this paper, we deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the great success of deep learning, especially deep convolution neural networks (CNNs), some researches have proposed to estimate height information from a single aerial image by training a deep CNN model with large-scale annotated datasets. These methods treat height estimation as a regression problem and directly use an encoder-decoder network to regress the height values. In this paper, we proposed to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem, using an ordinal loss for network training. To enable multi-scale feature extraction, we further incorporate an Atrous Spatial Pyramid Pooling (ASPP) module to extract features from multiple dilated convolution layers. After that, a post-processing technique is designed to transform the predicted height map of each patch into a seamless height map. Finally, we conduct extensive experiments on ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate significantly better performance of our method compared to the state-of-the-art methods.Comment: 5 pages, 3 figure

A dual network for super-resolution and semantic segmentation of sentinel-2 imagery

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.This work has been supported by the Spanish Research Agency (AEI) under project PID2020-117142GB-I00 of the call MCIN/AEI/10.13039/501100011033. L.S. would like to acknowledge the BECAL (Becas Carlos Antonio López) scholarship for the financial support.Peer ReviewedPostprint (published version

DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides

The following lists two main reasons for withdrawal for the public. 1. There are some problems in the method and results, and there is a lot of room for improvement. In terms of method, "Pre-trained Datasets (PD)" represents selecting a small amount from the online test set, which easily causes the model to overfit the online test set and could not obtain robust performance. More importantly, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to revisit the section of geological feature fusion, in terms of results, we need to further improve and refine. 2. arXiv is an open-access repository of electronic preprints without peer reviews. However, for our own research, we need experts to provide comments on my work whether negative or positive. I then would use their comments to significantly improve this manuscript. Therefore, we finally decided to withdraw this manuscript in arXiv, and we will update to arXiv with the final accepted manuscript to facilitate more researchers to use our proposed comprehensive and general scheme to recognize and segment seismic landslides more efficiently.Comment: 1. There are some problems in the method and results, and there is a lot of room for improvement. Overall, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to further improve and refine the results. 2. For our own research, we need experts to provide comments on my work whether negative or positiv

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation

DEEP FULLY RESIDUAL CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC IMAGE SEGMENTATION

Department of Computer Science and EngineeringThe goal of semantic image segmentation is to partition the pixels of an image into semantically meaningful parts and classifying those parts according to a predefined label set. Although object recognition models achieved remarkable performance recently and they even surpass human???s ability to recognize objects, but semantic segmentation models are still behind. One of the reason that makes semantic segmentation relatively a hard problem is the image understanding at pixel level by considering global context as oppose to object recognition. One other challenge is transferring the knowledge of an object recognition model for the task of semantic segmentation. In this thesis, we are delineating some of the main challenges we faced approaching semantic image segmentation with machine learning algorithms. Our main focus was how we can use deep learning algorithms for this task since they require the least amount of feature engineering and also it was shown that such models can be applied to large scale datasets and exhibit remarkable performance. More precisely, we worked on a variation of convolutional neural networks (CNN) suitable for the semantic segmentation task. We proposed a model called deep fully residual convolutional networks (DFRCN) to tackle this problem. Utilizing residual learning makes training of deep models feasible which ultimately leads to having a rich powerful visual representation. Our model also benefits from skip-connections which ease the propagation of information from the encoder module to the decoder module. This would enable our model to have less parameters in the decoder module while it also achieves better performance. We also benchmarked the effective variation of the proposed model on a semantic segmentation benchmark. We first make a thorough review of current high-performance models and the problems one might face when trying to replicate such models which mainly arose from the lack of sufficient provided information. Then, we describe our own novel method which we called deep fully residual convolutional network (DFRCN). We showed that our method exhibits state of the art performance on a challenging benchmark for aerial image segmentation.clos