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

The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in computer vision and in remote sensing to match features between images or to localize and recognize objects. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. In this paper, we introduce a SIFT-like algorithm specifically dedicated to SAR imaging, which is named SAR-SIFT. The algorithm includes both the detection of keypoints and the computation of local descriptors. A new gradient definition, yielding an orientation and a magnitude that are robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, as compared with existing approaches. We present an application of SAR-SIFT to the registration of SAR images in different configurations, particularly with different incidence angles.

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Sar-sift: a sift-like algorithm for sar images

Automatic detection of geospatial targets in cluttered scenes is a profound challenge in the field of aerial and satellite image analysis. In this paper, we propose a novel practical framework enabling efficient and simultaneous detection of multi-class geospatial targets in remote sensing images (RSI) by the integration of visual saliency modeling and the discriminative learning of sparse coding. At first, a computational saliency prediction model is built via learning a direct mapping from a variety of visual features to a ground truth set of salient objects in geospatial images manually annotated by experts. The output of this model can predict a small set of target candidate areas. Afterwards, in contrast with typical models that are trained independently for each class of targets, we train a multi-class object detector that can simultaneously localize multiple targets from multiple classes by using discriminative sparse coding. The Fisher discrimination criterion is incorporated into the learning of a dictionary, which leads to a set of discriminative sparse coding coefficients having small within-class scatter and big between-class scatter. Multi-class classification can be therefore achieved by the reconstruction error and discriminative coding coefficients. Finally, the trained multi-class object detector is applied to those target candidate areas instead of the entire image in order to classify them into various categories of target, which can significantly reduce the cost of traditional exhaustive search. Comprehensive evaluations on a satellite RSI database and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness and efficiency of the proposed work.

Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding

Vessel detection and classification from spaceborne optical images: A literature survey.

We present a novel Object Recognition approach based on affine invariant regions. It actively counters the problems related to the limited repeatability of the region detectors, and the difficulty of matching, in the presence of large amounts of background clutter and particularly challenging viewing conditions. After producing an initial set of matches, the method gradually explores the surrounding image areas, recursively constructing more and more matching regions, increasingly farther from the initial ones. This process covers the object with matches, and simultaneously separates the correct matches from the wrong ones. Hence, recognition and segmentation are achieved at the same time. The approach includes a mechanism for capturing the relationships between multiple model views and exploiting these for integrating the contributions of the views at recognition time. This is based on an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces. Integration is achieved by measuring the consistency of configurations of groups arising from different model views. Experimental results demonstrate the stronger power of the approach in dealing with extensive clutter, dominant occlusion, and large scale and viewpoint changes. Non-rigid deformations are explicitly taken into account, and the approximative contours of the object are produced. All presented techniques can extend any view-point invariant feature extractor.

Simultaneous object recognition and segmentation from single or multiple model views

This letter presents a new method for airport detection from large high-spatial-resolution IKONOS images. To this end, we describe airport by a set of scale-invariant feature transform (SIFT) keypoints and detect it using an improved SIFT matching strategy. After obtaining SIFT matched keypoints, to both discard the redundant matched points and locate the possible regions of candidates that contain the target, a novel region-location algorithm is proposed, which exploits the clustering information from matched SIFT keypoints, as well as the region information extracted through the image segmentation. Finally, airport recognition is achieved by applying the prior knowledge to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.

Airport Detection From Large IKONOS Images Using Clustered SIFT Keypoints and Region Information

The invention relates to an identification method of a remote sensing image building, belonging to an identification method of a remote sensing target image; the method overcomes the problems of false detection and miss-detection of the building in the existing detecting method of the building for improving the accurate detecting rate. The identification method sequentially comprises the following steps: region division, initial extraction of the building, road filtration and following extraction of the building. The identification method has good stability and strong environment adaptive capacity, and can solve the problem that the buildings and the roads close to the adjacent optical spectrum are confused, and the method can detect the building target with different shape structure and optical spectrum characteristics in the same image, the extracting result of the building has high accurate rate and good robustness, and the method can effectively extract various building targets under the remote sensing images with high resolution, therefore, the identification method has practical value in the construction of a geographical information system and a digital city system.

Identification method of remote sensing image building

An efficient two-scan connected component labelling (CCL) algorithm is proposed for a general purpose graphics processing unit (GPGPU). Compared to other GPU CCL algorithm, this algorithm has three distinct features. First, block-based and run-based strategies are combined in the first scan to simplify the equivalence label resolving process. Secondly, a novel labelling method for the GPU is introduced by constructing a forest of rooted trees using only 16-bit value for each node. Thirdly, the whole algorithm can be implemented in the GPU shared memory and minimise global memory bandwidth consumption. Experiments show that the algorithm achieves a speedup of between two and five times compared to other state-of-the-art GPU and CPU CCL algorithms.

Block-run-based connected component labelling algorithm for GPGPU using shared memory

The invention relates to an image object dividing method which belongs to an image data processing method and solves the problems of only consideration on color information, serious over-division phenomenon and difficult division of an integral area due to uneven lighting of the existing dividing methods The invention comprises: the step of establishing a double-size space, the step of crude division, the step of area combination, and the step of object division The invention effectively solves the problem of difficult integral area division during space color change for uneven lighting ina homogeneous area in image division; the area combination is automatically stopped by the method of minimizing cost function, and the robustness of the algorithm is strengthened; division results accord with human visual sensing characteristics, can be used for search and analysis based on image objects, and greatly improves the usability of division technology

Image object segmentation method

This paper presents a new method for urban detection from high-spatial-resolution satellite images. Unlike traditional approaches using only texture information for urban detection, we integrate several complementary image features through multiple Kernel Learning framework, and demonstrate that fusing multiple features can help improving urban detection accuracy rate. Furthermore, since that most of supervised urban classification approaches are mainly based on block-based image interpretation, the resulting urban boundary is very coarse. To handle this, we formulate the urban boundary refinement as a binary labeling problem, and propose a graph cut based approach to solve it. Experimental results show that the proposed approach outperforms the existing algorithm in terms of detection accuracy.

Chao Tao

Papers

Airport Detection From Large IKONOS Images Using Clustered SIFT Keypoints and Region Information

Identification method of remote sensing image building

Block-run-based connected component labelling algorithm for GPGPU using shared memory

Image object segmentation method

Urban area detection using multiple Kernel Learning and graph cut