Superpixel-Based 3D Building Model Refinement and Change Detection, Using VHR Stereo Satellite Imagery

Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm.

Semantic segmentation of remote sensing images enables in particular land-cover map generation for a given set of classes. Very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many tasks, from object recognition to semantic labelling, including the classification of Very High Resolution (VHR) satellite images. However, while plethora of works aim at improving object delineation on geographically restricted areas, few tend to solve this classification task at very large scales. New issues occur such as intra-class class variability, diachrony between surveys, and the appearance of new classes in a specific area, that do not exist in the predefined set of labels. Therefore, this work intends to (i) perform large scale classification and to (ii) expand a set of land-cover classes, using the off-the-shelf model learnt in a specific area of interest and adapting it to unseen areas.

Case instance segmentation of small farmland based on Mask R-CNN of feature pyramid network with double attention mechanism in high resolution satellite images

Spatio-temporal analysis of Kilim River using Very High-Resolution (VHR) satellite image between the years of 2005 and 2012 are vital to assess river morphological changes and erosion detection over the specific time duration. Therefore, this study utilizes remote sensing and Geographical Information System (GIS) capabilities to identify the channel migration and shifting as well as to calculate the rate of erosion and accretion. Classification of the river from Quickbird and WorldView-2 images is carry out using Maximum Likelihood Classification (MLC) technique whereas feature selection tool performs to extract river feature from the raw image. Subsequently, river feature been overlaid to identify the changes of riverbank pattern and to calculate the rate of riverbank erosion in terms of area. The result shows the total area of erosion is 27252.959 m2 and the total area of accretion is 6079.999 m2. A maximum erosion of 6993.102 m2 detected at Section E while maximum accretion of 681.026 m2 spotted at Section D. The rates of riverbank erosion are 3406.62 m2/year while the rates for accretion are 759.999 m2/years between 2005 and 2012. Hence, the output of this study enables Langkawi Development Authority (LADA) and other stakeholders to recognize the specific location, which severely affected by erosion and accretion as well as to spot the area experienced huge river channel shifting.

Pléiades satellite images for deriving forest metrics in the Alpine region

Application of high-resolution stereo satellite images to detailed landslide hazard assessment

This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and optimally planning further aerial surveys. A label propagation mechanism is adopted to reliably propagate the labeled pixels annotated by the user on the aerial image by visual photointerpretation to the (lower resolution) satellite image. The experimental analysis is carried out using post-disaster images of the 2010 Yushu earthquake in China. The obtained results confirm that the proposed method can significantly reduce the user annotation effort and the cost for acquiring additional aerial images leading to more accurate and timely debris detection maps.

Satellite imagery has been used to improve scientific research worldwide. In this study, the southern right whale (Eubalaena australis) was chosen to test the use of medium, high, and very high resolution (VHR) satellite images, on the Brazilian breeding ground. These images were used to identify the whales and were compared to aerial survey data collected in the same area. The VHR satellite images from the Pleiades‐1A satellite, available on Google Earth, displayed the best results when compared to those from Sentinel 2, Landsat 8, Rapid Eye, and Planet Scope. No significant differences were observed (Mann‐Whitney U test) between the possible whales recorded in the satellite images and of real whales recorded in situ by aerial surveys, while considering either the number of groups (p = .841, n = 5) or the total number of animals (p = .222, n = 5). Further, when using VHR images, the geographical positions of the whales recorded in situ had a positive correlation with the positions generated from the satellite images (Mantel test: r = 0.52, p = .001, n = 13). This technique may represent an important tool for detecting right whales, especially in countries where research funding is scarce.

As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods.

A new algorithm based on the fusion of edge and multispectral information is proposed for the pixel-wise classification of very high-resolution (VHR) remotely sensed imagery. It integrates the multispectral, spatial and structural information existing in the image. The edge feature is first extracted using an improved multispectral edge detection method, which takes into account the original multispectral bands, the linear NDVI, and the independent spectral components extracted by independent component analysis (ICA). Direction-lines are then defined using the edge and multispectral information. Two effective spatial measures are calculated based on the direction-lines in order to describe the contextual information and strengthen the multispectral feature space. Then, the support vector machine (SVM) is employed to classify the hybrid structural-multispectral feature set. In experiments, the proposed spatial measures were compared with the pixel shape index (PSI) and the gray level co-occurrence matrix (GLCM). The experimental results show that the proposed algorithm performs well in terms of classification accuracies and visual interpretation.

Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach

Reliable building delineation in very high resolution (VHR) satellite imagery can be achieved by precise disparity information extracted from stereo pairs. However, off-nadir VHR images over urban areas contain many occlusions due to building leaning that creates gaps in the extracted disparity maps. The typical approach to fill these gaps is by interpolation. However, it inevitably degrades the quality of the disparity map and reduces the accuracy of building detection. Thus, this research proposes a registration-based technique for mapping the disparity of off-terrain objects to avoid the need for disparity interpolation and normalization. The generated disparity by the proposed technique is then used to support building detection in off-nadir VHR satellite images. Experiments in a high-rise building area confirmed that 75 percent of the detected building roofs overlap precisely the reference data, with almost 100 percent correct detection. These accuracies are substantially higher than those achieved by other published research.

Third-dimension information is of a great importance for several remote sensing applications, such as building detection. The main data-source for these applications is very high resolution (VHR) satellite images which allow detailed mapping of complex environments. Stereo VHR satellite images allow the extraction of two correlated types of third-dimension information: disparity and elevation information. While the disparity is measured directly, the elevation information is derived computationally. To measure the disparity information, two overlapped images are matched. However, for the backward and forward off-nadir VHR stereo images, building facades occlude areas and hence create many data gaps. When the disparity is required to represent only the off-terrain objects, interpolation and normalization techniques are typically used. However, in dense urban environments, these techniques destroy the quality of the generated data. Therefore, this paper proposes a registration-based technique to measure the disparity of the above-ground objects. The technique includes constructing epipolar images and registering them using common terrain- level features to allow direct disparity mapping for the off-terrain objects. After the implementation, the negative effects of occlusion in the off-nadir VHR stereo images are mitigated through direct disparity mapping of the above-ground objects and bypassing the interpolation and normalization steps.

OpenStreetMap(OSM) provides extensive coverage of road network that can be a source to prepare training samples for automated road mapping using very high resolution(VHR) satellite images and machine learning. However, several studies have shown that the pervasive spatial misalignment between OSM vector data and VHR images yields poor quality training samples and thereby compromises the performance of models. In this study, we undertake to address this shortcoming and develop an automated line segment shifting workflow to yield OSM vector data that aligns with VHR road features to generate high quality training samples. The approach leverages the standard deviation differences of road pixels and background information to guide the transformations. By taking into account trees, shadows, cars and water body on or beside the road when STD was calculated, our method is robust to various road obstacles. Experimental validations are conducted to confirm the correctness of aligned OSM data showing up to 338% improvement compared with original OSM. Finally, based on visual inspection, the road map generated by aligned OSM also presents obvious quality improvement in comparison with map created by original OSM.

The orthorectification of very high resolution (VHR) satellite images is a fundamental process to ensure the interoperability of the spatial information obtained from them. Therefore, previous studies have used different types of inputs, including multiple sources of control points and digital elevation models (DEM) of all kinds, in addition to testing different optimization methods. This research aims to jointly use and evaluate the use of the evolutionary algorithm Particle Swarm Optimization (PSO), stereoscopic points from photogrammetric blocks and DEM from different sources, to obtain cartographic products of scale 1:10.000 comparing their results with those obtained by ordinary least squares from previous experiments. The methodological proposal includes the entire procedure for the estimation and selection of the coefficients of the Rational Functional Model (RFM) spatial arrangement model using Particle Swarm Optimization and the Feature Condition Analysis method together with all the flow necessary for the generation of the final orthoimage. As a result, it is observed that the use of the PSO improves on average by 3% the positional accuracy of the orthoimage, however its use requires high computational resources and also this type of optimization method is not available yet in specialized software, being difficult for massive implementation in cartographic production processes.