Plane and ship targets in satellite stereo images has poor stereo matching effect due to complex structure and scarcity of samples. To solve this problem, inspired by the similar representation of the same target in optical images and disparity maps, combined with prior knowledge of different components of plane and ship targets, this paper proposes a stereo matching method for plane and ship targets based on component constraint. Firstly, a generalized Hough transform segmentation method with hit ratio and uniformity ratio constraints is constructed to extract component blocks. Then, the matching cost is matched using the component block constraint. Finally, the disparity is determined by iteratively optimizing the matching cost. The experimental results show that the proposed stereo matching method NMAD is 1.4m, and RMSE is better than 5m. More advanced performance than other methods.

Considering the limitations of stereo satellite resources and imaging quality, as well as the challenges of occlusion and disparity discontinuity impacting the matching results, this paper proposes a novel generalized stereo matching method based on topological structure consistency for urban 3D reconstruction. Firstly, the topological structure within the corresponding superpixel neighborhood of the left and right views is constructed. Then, the topological structure consistency cost (TSC) is proposed to evaluate the similarity of the topological structure by combining two measurement methods of grayscale spatial distance and spatial relative relationship. Finally, iteratively optimizing the matching cost through visibility term and disparity discontinuity term to output the disparity map. The experimental results show that the proposed stereo matching method achieves normalized median absolute deviation (NMAD) better than 1.5 m and root mean square error (RMSE) better than 3.5 m, which can realize more advanced performance compared with the state-of-the-art methods.

Low-quality digital surface model (DSM) and blurred textures result in poor visualization of building reconstructions. In this paper, an edge-based block decomposition method for building rooftop reconstruction and side walls generation is proposed, which is applied in three-dimensional (3D) reconstruction texture mapping of buildings. The rooftop is decomposed into blocks according to the contour of the building's rooftop in the orthophoto. Each block is combined with the DSM generated by the stereo pair of satellite images to reconstruct the rooftop and interpolate to generate the side walls to obtain a regularized 3D point cloud of the building. For better texture mapping effect, single image super-resolution (SISR) method is used to enhance the texture details of remote sensing images. Experimental results show that the proposed method has better visualization effect than other 3D reconstruction methods based on satellite data.

Multiple observation data from multisensor satellites lead to data with complex heterogeneous tensor structures, and the available object data are observed by the arbitrary combination of multisource satellites, which brings significant challenges to object recognition using typical deep learning methods, classical support vector machines (SVMs), and support tensor machines (STMs). To process multisource data represented as heterogeneous tensors effectively, the heterogeneous STM (HSTM) is proposed by integrating rank-$R$decomposition of multitensors with nu-STM to directly separate samples in heterogeneous tensor space. Furthermore, combining the shared projecting tensors used to mine related information for intercombinations and the auxiliary projecting tensors exploited to capture the individual information for intracombination, the HSTM is extended to Adaptive HSTM (AHSTM) to address the arbitrary combination of multisource data by only training once. To accelerate the training of AHSTM, the AHSTM-oriented decomposition method is proposed to use small sequential analytic optimizations instead of the original large numerical optimization. Using multiangle optical and synthetic aperture radar (SAR) satellite images, experimental results demonstrate that the proposed AHSTM obtains higher accuracy than typical SVM and STM methods for each combination of multisource data and reduces the time consumption by over 75% for the training set with different sizes compared with the typical interior-point method and the active-set method.

Considering the multiple observations from satellites with different types of sensors, the available object data are regarded as different combinations of multisource heterogeneous data. To achieve accurate object recognition, the dimension reduction (DR) technology is important to capture the low-dimensional discriminative representation containing complementary information from multisource data, while off-the-shelf DR methods can only handle input represented as vector or homogeneous tensors and fail to deal with multisource data represented as heterogeneous tensors. In addition, the existing DR methods are classifier-independent, making it difficult to ensure effective recognition under a specific classifier. To solve these problems, the DR method for Heterogeneous Tensors based on Graph-based and Classifier-oriented Embedding (HTGCE) method is proposed to learn the low-dimensional representation of multisource data using different combinations of samples. First, self-learning adjacency matrices are constructed to capture the local structure of different combinations of multisource data autonomously. Then, unlike the classifier-independent discriminant term used in existing DR methods, a classifier-oriented discriminant term is constructed to enhance the specific classifier-based recognition results. Furthermore, the reconstruction error minimization term is created to enable DR results to inherit the main information of the original data. Moreover, an adaptive weight factor is built to balance the importance of different sources for object recognition. Finally, an alternative optimization strategy is presented to solve the optimization problem of HTGCE. Using the multiresolution multiangle optical dataset and the paired optical and SAR dataset, the experimental results demonstrate that the HTGCE outperforms typical vector- and tensor-based DR methods in terms of recognition accuracy.

In the above article[1], some errors appear in equations due to incorrect fonts. First, the matrix variables should be denoted by uppercase boldface letters, e.g.,$\boldsymbol{U}_{l}^{m}$and$\boldsymbol{S}_{g}$. The vector variables should be denoted as lowercase boldface letters, e.g.,$c$and$c^{\prime}$.

It is challenging to achieve high accuracy and rich details three-dimensional (3D) reconstruction of ground object using satellite remote sensing image (RSI). A 3D reconstruction method is proposed to address this problem based on multi-view 3D occupancy network. The domain adaptive multi-view image obtained by pretreatment is input into the depth residual network to extract perceptual features at first. The occupancy probability of objects at different coordinates in 3D space is obtained by combining spatial probability network with definition balance strategy. Then, the isosurface is extracted to obtain the 3D mesh of the object, and the reconstruction result is given scale by combining the attitude estimation and the original RSI parameter information. Experiments are carried out on a testing dataset composed of hundreds of object image pairs. The experimental results show that the proposed method for satellite RSI has better accuracy than the typical single-view and multi-view reconstruction methods.

To study a new framework for building detection from optical remote sensing image(RSI) that based on rectangle approximation and region growing. According to the geometric structures of buildings, a right angle detection method based on voting strategy is investigated to get building candidates. In order to accurately estimate the shape of buildings with non-convex properties, an algorithm is employed to reduce false positives by extracting outer corners of buildings from the detected right angles. Combining with similar spectral features of surfaces, a segmentation algorithm based on region growing is proposed to get the complete top surfaces of buildings. Using several RSIs of Florida and Nebraska areas in WorldView-3 image data, experimental results show that the proposed method achieves the precision ratio of 89.44% and recall ratio of 90.36%.

Hierarchical detection from parking lot to vehicle based on visual saliency and angle estimation is proposed for the large-area remote sensing image (RSI). According to the brightness characteristics of parking lot, an accelerated bright-based saliency map (BBSM) is presented to locate the parking lot, which is also achieved by two-step location from rough to accurate. The suspected vehicle queues are then detected using the spectral residual saliency map and rotated to the horizontal direction by angle estimation of vehicle queue. Each vehicle in the suspected vehicle queues is identified by HOG features extraction and SVM classification. Using several RSIs at a spatial resolution of 0.5 meter with the size of 24000×24000 pixels, experimental results show that the proposed method outperforms two traditional methods and achieves the precision ratio of 87.1% and recall ratio of 90.2%.

In airplane target detection, there was the drawback of weak recognition ability for dark targets and high false alarm rate for detected targets. In order to address the problem, we proposed a detection method based on SAR and optical image feature fusion. It extracted texture, moment and backscattering characteristics from SAR images and combined with optical features. Moreover, the novel airplane edge templates incorporating SAR and optical images were created to acquire saliency map. During the process of detection, first, the saliency map and the One-Class-SVM (OCSVM) classifier were used to initially recognize the suspected airplane targets. Then, the combination features were adopted to further identify the misidentified airplane target. The experimental results showed that the Precision of the proposed method was 61.82% and the False Alarm Rate was 20%, which was better than the HIS-based detection method.

Inshore ship detection becomes challenging in high-resolution optical remote sensing image (RSI) because inshore ships are often incomplete and deformed due to the poor imaging condition and shadow of ship superstructure, and there are various interferences in harbor. A contour refinement and the improved generalized Hough transform (GHT)-based inshore ship detection scheme is proposed for RSI with complex harbor scenes. First, the suspected region of ships (SRS) is located in the entire RSI according to the line segments of ship body and docks. The contours in each SRS are then refined to repair the damaged ship head contour (SHC) using the convex set characteristics of ship head and subsequently reduce non-SHC by curvature filtering. In each refined SRS, equal frequency quantification instead of equal width quantification for R-Table construction and Gini coefficient-based decision criterion combining the number and distribution of votes are proposed to improve GHT (i.e., EG-GHT) and to extract SHCs as candidate targets. The false candidates are removed according to pixel proportion described by the structured binarization feature. Applying the border scoring strategy, the best candidates with the largest score among all the overlapped bounding boxes are selected as the final detection targets. Using the public RSIs with various cases, including turbid water, cloud occlusion, ships moored together, and ships with the different sizes, experimental results demonstrate the proposed scheme outperforms state-of-the-art contour-based methods and deep learning-based methods in terms of precision-recall rate and average precision, respectively.

It is challenging to distinguish the intensive parked vehicles in the parking lot for the high-resolution satellite images even at a spatial resolution of 0.5 meter. A vehicle segmentation and detection algorithm is proposed to address this problem based on the improved Mean Shift algorithm with the adaptive parameter adjustment. The suspected vehicle areas are first located using the regional growth with the seed point determined by the simplified Itti algorithm and non-maximal suppression. An adaptive parameter adjustment based Mean Shift method is presented to divide each suspected vehicle area into the suspected vehicle slices. To reduce the false alarms, feature vectors contained spectral and texture features are constructed and used in the classification based on support vector machine to distinguish between vehicles and non-vehicles. Experimental results on several remote sensing images at a spatial resolution of 0.5 meter present the accurate detection results for the intensive parked vehicles.

In order to detect airplane efficiently in large scene remote sensing images with high spatial resolution, a hierarchical detection framework is proposed according to the process of locating the target area in the downsampled image and then detecting the target in the original high-resolution image of that target region. First, we locate the airport in the downsampled original high-resolution image based on line features (midpoint coordinate and angle) clustering. A visual saliency model based on Itti model is then applied to detect airplanes candidates in the resulting airport region with the original high-resolution image. Finally, scale invariant feature transformation and support vector machine are used to classify the airplanes candidates. Experimental results indicate that the proposed method presents higher recall rate and detection speed. Meanwhile, the time cost of airport detection is much less than representative methods.