Object detection in high spatial resolution remote sensing images (HSRIs) is an important part of image information automatic extraction, analysis, and understanding. The region of interest (ROI) scale of object detection and the object feature representation are two vital factors in HSRI object detection. With respect to these two issues, this article presents a novel HSRI object detection method based on convolutional neural networks (CNNs) with suitable object scale features. First, the suitable ROI scale of object detection is obtained by compiling statistics for the scale range of objects in HSRIs. Then, a CNN framework for object detection in HSRIs is designed using a suitable ROI scale of object detection. The object features obtained using a CNN have good universality and robustness. Finally, a CNN framework with a suitable ROI scale of object detection is trained and tested. Using the WHU-RSONE data set, the proposed method is compared with the faster region-based CNN (Faster-RCNN) framework. The experimental results show that the proposed method outperforms the Faster-RCNN framework and provides good object detection results in HSRIs.

With the successful launch of GaoFen4 (GF4), on-orbit high accuracy geometric calibration for the high-resolution geostationary optical satellite will be a new research topic. With the improvement in the geometric resolution from geostationary orbit, it will become more and more difficult to meet the requirements of both high geometric resolution and large coverage for the available reference data. The purpose of this paper is to explore a new self-calibration mode for GF4 and future high-resolution geostationary optical area array cameras based on the fewest ground control points (GCPs). To overcome the problems of overparameterization, strong correlation and lower significance of the traditional rigorous imaging model, the simplified physical internal model is proposed, and its effectiveness in describing and compensating for the camera internal distortion is verified. Based on the simplified physical internal model, the self-calibration method based on two GCPs and evenly distributed tie points of two images is proposed for the high accuracy estimation of the calibration parameters. The GCPs can be used to provide the absolute geographical constraints for scale information, and the tie points can be used to provide the global constraints for optimum estimation. After calibration, the internal distortion is well compensated, and the positioning accuracy with relatively few GCPs is shown to be better than 1.0 pixels for both the panchromatic and near-infrared sensor and the intermediate infrared sensor. This paper will provide a new usable concept and approach for the future higher resolution geostationary area array optical camera to overcome the stringent requirements of both high resolution and a large area of reference data for the traditional calibration method.

Due to the all-weather and all-day advantage and the high geometric accuracy, the spaceborne synthetic aperture radar (SAR) stereo pair has a wide application in digital elevation model (DEM) production and ground control points (GCPs) extraction around the world. The GaoFen3 (GF3) remote sensing satellite is the first C-band multipolarization SAR satellite with a resolution of 1 m in China and plays an important role in commercial exploitation and scientific research. This letter performs a comprehensive analysis of the geometric accuracy for GF3 stereo pair orientation. By analyzing the orientation error sources, it is proven that the affine transformation model in image space can effectively compensate the unmodeled errors in GF3 imagery. Based on the rational polynomial coefficient model, the integrated orientation model for the GF3 stereo pair is presented here. Moreover, the model parameters and weight determination are further studied to analyze the influence of weight matrixes on geometric accuracy. Experimental results demonstrate that the proposed integrated orientation model can be effectively used for the GF3 stereo pair. The GCPs, convergent angle, and weight setting are the key factors influencing the geometric accuracy.

Object detection in high-spatial-resolution remote sensing images (HSRIs) is an import part of the automatic extraction and understanding of image information in high-resolution earth observation systems. Regarding how to achieve optimal block object detection for large-scale HSRIs, this paper proposes a multiscale block fusion object detection method for large-scale HSRIs. First, the objects in large-scale HSRIs are detected using different block scales, and the average precision (AP) of the different object detection results is counted at different block scales. Then, according to the statistical information, the image block scales corresponding to the optimal AP value of the different objects are obtained. Finally, a soft non-maximum suppression algorithm is used to fuse the image block scale detection results corresponding to the optimal AP values of the different objects, to obtain the object detection results of the large-scale HSRIs. The experimental results confirm that the proposed method outperforms all other single-scale image block detection methods and provides acceptable object detection results in large-scale HSRIs.

Crack junction is the crossing or branching point of different cracks in the pavement image. It represents the branch of transverse crack or longitudinal crack, and describes the interlaced network of alligator crack. It is a simple yet important factor to characterize the type and severity level of crack. This paper is motivated to robustly detect crack junctions of any type and size in pavement image, regardless of the pavement interferences. In this paper, the contrast between the crack junction and pavement background is first enhanced by removing the large interferences and background. Then, based on the structure characteristic of crack curves, correlation structure index is proposed to locate candidates of crack junctions. Actual junctions are extracted among the candidates with the unified ball tensor structure after the iterative tensor voting. The proposed method is tested with the concrete pavement images of public data set of SDNET2018 and asphalt pavement images collected by the unmanned aerial vehicle on the highway G45 in China. Experimental results demonstrate that the proposed method can detect crack junctions with the correctness of 0.891 and completeness of 0.887. It can be applied to junction detection on concrete and alligator pavement with different noise and interference, and is promising to classify the crack type and quantify the severity level.