Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

A survey on object detection in optical remote sensing images

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. Machine-accessible metadata file describing the reported data (ISA-Tab format)

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Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

Results. The incidence rate of significant non-cardiac occlusive stenotic lesions in patients with coronary artery disease (CAD), who had to undergo CABG, was 15,84 %. Simultaneous revascularization of coronary and non-coronary arteries was performed in 2,46 % of patients with CAD and PolyVD and multi-stage surgical proced ures were chosen in other cases. Conclusions. The outcomes of CAD surgical treatment were improved in this group of patients due to the implementation of a mul-

Федеральное государственное бюджетное учреждение «Научно-исследовательский институт комплексных проблем сердечно-сосудистых заболеваний» Сибирского отделения Российской академии медицинских наук, Кемерово, Россия

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.

Accurate road detection and centerline extraction from very high resolution (VHR) remote sensing imagery are of central importance in a wide range of applications. Due to the complex backgrounds and occlusions of trees and cars, most road detection methods bring in the heterogeneous segments; besides for the centerline extraction task, most current approaches fail to extract a wonderful centerline network that appears smooth, complete, as well as single-pixel width. To address the above-mentioned complex issues, we propose a novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks. Specifically, CasNet consists of two networks. One aims at the road detection task, whose strong representation ability is well able to tackle the complex backgrounds and occlusions of trees and cars. The other is cascaded to the former one, making full use of the feature maps produced formerly, to obtain the good centerline extraction. Finally, a thinning algorithm is proposed to obtain smooth, complete, and single-pixel width road centerline network. Extensive experiments demonstrate that CasNet outperforms the state-of-the-art methods greatly in learning quality and learning speed. That is, CasNet exceeds the comparing methods by a large margin in quantitative performance, and it is nearly 25 times faster than the comparing methods. Moreover, as another contribution, a large and challenging road centerline data set for the VHR remote sensing image will be publicly available for further studies.

Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network

A simple method for fitting of bounding rectangle to closed regions

Extraction of map objects such roads, rivers and buildings from high resolution satellite imagery is an important task in many civilian and military applications. We present a semi-automatic approach for road detection that achieves high accuracy and efficiency. This method exploits the properties of road segments to develop customized operators to accurately derive the road segments. The customized operators include directional morphological enhancement, directional segmentation and thinning. We have systematically evaluated the algorithm on a variety of images from IKONOS, QuickBird, CARTOSAT-2A satellites and carefully compared it with the techniques presented in literature. The results demonstrate that the algorithm proposed is both accurate and efficient.

Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques

Extraction of features from images has been a goal of researchers since the early days of remote sensing. While significant progress has been made in several applications, much remains to be done in the area of accurate identification of high-level features such as buildings and roads. This paper presents an approach for detecting bridges over water bodies from multispectral imagery. The multispectral image is first classified into eight land-cover types using a majority-must-be-granted logic based on the multiseed supervised classification technique. The classified image is then categorized into a trilevel image: water, concrete, and background. Bridges are then recognized in this trilevel image by using a knowledge-based approach that exploits the spatial arrangement of bridges and their surroundings using a five-step approach. A river extraction module identifies the rivers using a recursive scanning technique and geometric constraints. Using a neighborhood operator and the knowledge of the spatial dimensions of a typical bridge, we identify the possible bridge pixels. These potential bridge pixels are then grouped into possible bridge segments based on their connectivity and geometric properties. Finally, these bridge segments are verified on the basis of directional water index along different directions and their connectivity with the road segments. The approach proposed in this paper has been implemented and tested with images from the IRS-1C/1-D satellite that has a spatial resolution of 23.5 23.5 m. The results show that this approach is both efficient and effective in extracting bridges.

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An Automatic Bridge Detection Technique for Multispectral Images

Automatic building extraction remains an open research topic in digital photogrammetry and remote sensing. While many algorithms have been proposed for building extraction, none of them solve the problem completely. This is even a greater challenge in urban areas, due to high-object density and scene complexity. Standard approaches do not achieve satisfactory performance, especially with high-resolution satellite images. This paper presents a novel framework for reliable and accurate building extraction from high-resolution panchromatic images. Proposed framework exploits the domain knowledge (spatial and spectral properties) about the nature of objects in the scene, their optical interactions and their impact on the resulting image. The steps in the approach consist of 1) directional morphological enhancement; 2) multiseed-based clustering technique using internal gray variance (IGV); 3) shadow detection; 4) false alarm reduction using positional information of both building edge and shadow; and 5) adaptive threshold based segmentation technique. We have evaluated the algorithm using a variety of images from IKONOS and QuickBird satellites. The results demonstrate that the proposed algorithm is both accurate and efficient.

Debasis Chaudhuri

Papers

A simple method for fitting of bounding rectangle to closed regions

Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques

An Automatic Bridge Detection Technique for Multispectral Images

Automatic Building Detection From High-Resolution Satellite Images Based on Morphology and Internal Gray Variance

A modified metric to compute distance