Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection

The generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed

A Framework for SAR-Optical Stereogrammetry over Urban Areas

Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are required to integrate the different properties of each sensor and produce useful information. For example, synthetic aperture radar (SAR) data can be fused with optical imagery to produce 3D information using stereogrammetric methods. The main focus of this study is to investigate the possibility of applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical image pairs. For this purpose, the applicability of semi-global matching is investigated in this unconventional multi-sensor setting. To support the image matching by reducing the search space and accelerating the identification of correct, reliable matches, the possibility of establishing an epipolarity constraint for VHR SAR-optical image pairs is investigated as well. In addition, it is shown that the absolute geolocation accuracy of VHR optical imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be improved by a multi-sensor block adjustment formulation based on rational polynomial coefficients. Finally, the feasibility of generating point clouds with a median accuracy of about 2m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.Comment: This is the pre-acceptance version, to read the final version, please go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirec

WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images

Stereo matching of high-resolution satellite images (HRSI) is still a fundamental but challenging task in the field of photogrammetry and remote sensing. Recently, deep learning (DL) methods, especially convolutional neural networks (CNNs), have demonstrated tremendous potential for stereo matching on public benchmark datasets. However, datasets for stereo matching of satellite images are scarce. To facilitate further research, this paper creates and publishes a challenging dataset, termed WHU-Stereo, for stereo matching DL network training and testing. This dataset is created by using airborne LiDAR point clouds and high-resolution stereo imageries taken from the Chinese GaoFen-7 satellite (GF-7). The WHU-Stereo dataset contains more than 1700 epipolar rectified image pairs, which cover six areas in China and includes various kinds of landscapes. We have assessed the accuracy of ground-truth disparity maps, and it is proved that our dataset achieves comparable precision compared with existing state-of-the-art stereo matching datasets. To verify its feasibility, in experiments, the hand-crafted SGM stereo matching algorithm and recent deep learning networks have been tested on the WHU-Stereo dataset. Experimental results show that deep learning networks can be well trained and achieves higher performance than hand-crafted SGM algorithm, and the dataset has great potential in remote sensing application. The WHU-Stereo dataset can serve as a challenging benchmark for stereo matching of high-resolution satellite images, and performance evaluation of deep learning models. Our dataset is available at https://github.com/Sheng029/WHU-Stere

Multi-label learning based semi-global matching forest

Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

Semantic Mapping of Road Scenes

The problem of understanding road scenes has been on the fore-front in the computer vision community for the last couple of years. This enables autonomous systems to navigate and understand the surroundings in which it operates. It involves reconstructing the scene and estimating the objects present in it, such as ‘vehicles’, ‘road’, ‘pavements’ and ‘buildings’. This thesis focusses on these aspects and proposes solutions to address them. First, we propose a solution to generate a dense semantic map from multiple street-level images. This map can be imagined as the bird’s eye view of the region with associated semantic labels for ten’s of kilometres of street level data. We generate the overhead semantic view from street level images. This is in contrast to existing approaches using satellite/overhead imagery for classification of urban region, allowing us to produce a detailed semantic map for a large scale urban area. Then we describe a method to perform large scale dense 3D reconstruction of road scenes with associated semantic labels. Our method fuses the depth-maps in an online fashion, generated from the stereo pairs across time into a global 3D volume, in order to accommodate arbitrarily long image sequences. The object class labels estimated from the street level stereo image sequence are used to annotate the reconstructed volume. Then we exploit the scene structure in object class labelling by performing inference over the meshed representation of the scene. By performing labelling over the mesh we solve two issues: Firstly, images often have redundant information with multiple images describing the same scene. Solving these images separately is slow, where our method is approximately a magnitude faster in the inference stage compared to normal inference in the image domain. Secondly, often multiple images, even though they describe the same scene result in inconsistent labelling. By solving a single mesh, we remove the inconsistency of labelling across the images. Also our mesh based labelling takes into account of the object layout in the scene, which is often ambiguous in the image domain, thereby increasing the accuracy of object labelling. Finally, we perform labelling and structure computation through a hierarchical robust PN Markov Random Field defined on voxels and super-voxels given by an octree. This allows us to infer the 3D structure and the object-class labels in a principled manner, through bounded approximate minimisation of a well defined and studied energy functional. In this thesis, we also introduce two object labelled datasets created from real world data. The 15 kilometre Yotta Labelled dataset consists of 8,000 images per camera view of the roadways of the United Kingdom with a subset of them annotated with object class labels and the second dataset is comprised of ground truth object labels for the publicly available KITTI dataset. Both the datasets are available publicly and we hope will be helpful to the vision research community