Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

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

Pattern recognition

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)

/pdf/distributed-solar-photovoltaic-array-location-and-extent-46upvu2u5n.pdf

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

Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

https://www.isprs.org/proceedings/XXXV/congress/comm3/papers/300.pdf

Road detection from high-resolution satellite images using artificial neural networks

Rational Function Optimization using Genetic Algorithms

Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.

/pdf/3d-building-reconstruction-using-dense-photogrammetric-point-19x6va9faq.pdf

3D Building Reconstruction Using Dense Photogrammetric Point Cloud

Rational function models (RFMs) provide one of the best methods of extracting spatial information from high-resolution satellite images, particularly when sensor parameters cannot be accessed. RFM terms have no physical meaning, and hence, all of these terms are typically used in conventional solutions. As a result, more ground control points (GCPs) are required, and the model is prone to overparameterization. In this letter, a modified particle swarm optimization is applied to identify the optimal terms for RFMs. In comparison to conventional models, experimental results demonstrate how well the proposed algorithm can determine an RFM, which is optimal in both the total number of terms and the positional accuracy. The proposed algorithm is determined to be efficient when subpixel accuracy can be obtained with four GCPs in IKONOS-Geo image.

Mehdi Mokhtarzade

Papers

Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

Road detection from high-resolution satellite images using artificial neural networks

Rational Function Optimization using Genetic Algorithms

3D Building Reconstruction Using Dense Photogrammetric Point Cloud

Particle Swarm Optimization of RFM for Georeferencing of Satellite Images