The accurate matching of multisource, multi-temporal remote sensing images is challenging because of significant nonlinear intensity differences (NIDs) and severe geometric distortions. To address these problems, we developed a robust image matching method: oriented filter-based matching (OFM). OFM is insensitive to NIDs, while exhibiting scale and rotational invariance. First, salient feature points with multiscale attributes were detected in the Gaussian-scale space of the input images. Then, the images were convoluted using multi-oriented filters, and unified feature maps were constructed by the extraction of orientation indices using effective data pooling operations. The constructed feature maps were highly resistant to NIDs. Five filters were integrated into the OFM framework to investigate their applicabilities in different application scenarios. Next, a novel rotation-invariant feature descriptor was constructed, using a dominant direction determination approach and a descriptor-grouping strategy. The dominant direction determination approach enables accurate dominant direction estimation, whereas the descriptor-grouping strategy improves the stability of the method under different rotational angles. Finally, brute-force matching was implemented to obtain initial matches; an improved mismatch elimination method was used to identify reliable putative matches. To evaluate the performance of OFM, we created a large dataset comprising 4,427 pairs of multitemporal optical–optical, optical–synthetic aperture radar (SAR), optical–infrared, and optical–depth images. OFM outperformed state-of-the-art methods in terms of number of correct matches, recall, inlier ratio, root mean square error and success rate. Our implement is publicly available ¹ .

On-orbit geometric calibration (GC) is always performed to compensate for geometric distortion from the satellite’s camera. However, the traditional GC method is complex and difficult to apply broadly due to its reliance on a rigorous physical model (RPM), which involves not only complex processing of attitude, orbit, and time but also the transformation among multiple coordinate systems. In addition, the RPM is closely related to the designs of satellites and cameras, which increases the complexity of the GC and thus reduces generalizability. This article proposes a practical and robust GC method to prevent camera distortion based on a standardized rational polynomial coefficient (RPC) model. Through a series of innovations, including stepwise optimization, a priori gross error elimination, the adjustment model with angular resolution, and the correction for the bias field-of-view (FOV) distortion, we were able to achieve robust GC for camera distortion, as well as accurate splicing and registration among segmented images. Method validation using data from the linear-array camera of the ZiYuan3 (ZY-3)-02 satellite, and the area-array camera of the GaoFen-4 (GF4) satellite, produced satisfactory results, indicating that our method effectively compensates for systematic geometric distortion such that consistent GC and accuracy comparable with that of traditional RPM-based methods can be obtained.

Correcting the relative geometric errors is an essential step in the generation of digital ortho maps and digital surface models from GaoFen-7 (GF-7) regional images, in which the block adjustment (BA) technology is always adopted. However, the traditional BA methods are typically ineffective and not universal due to their dependence on sufficient control data. Although recently developed BA methods using a large number of observation images can substantially eliminate the additional control data, they are not suitable for the BA with few images in the absence of sufficient control data, which is a situation that is often encountered in practical applications. In such situation, the inevitable elevation error caused by the initial positioning error of images will transfer and accumulate in the horizontal direction of the block, resulting in the inconsistent geometric accuracies among images. Aiming at this problem, this article proposed a practical method of combining a stable free BA and a posteriori compensation. Through the stepwise correction and integration of geometric errors, the elevation error and its cumulative effect in free BA could be eliminated at a small control cost, and a robust result with consistent accuracy could be achieved, even with only a few observation images. This method was validated through experiments performed on GF-7 stereo images, and the satisfactory results indicated that it effectively restrained error accumulation and improved the geo-positioning consistency of regional GF-7 images.

Due to the limitations in the geometric accuracy of reference data and matching accuracy between images from different sensors, the conventional method of calibration may not perform well in terms of accuracy for an agile optical satellite (AOS) with submeter resolution. Due to the high mobility of an AOS, a cross-image pair (CIP) including a push-broom image and a swing scanning image for the same area could be collected. This letter developed a novel calibration method for an AOS, which is characterized using the constraints from the CIP instead of reference images covering a calibration site. In this method, the viewing angle of a charge-coupled device detector fit by two polynomials is introduced into a rigorous imaging model to establish the calibration model, and a bundle adjustment under the constraint of an aided digital surface model (DSM) is applied to solve the unstable calculation stemming from the strong correlation between the parameters of the coupled images. To verify the effectiveness of the method, experiments were conducted using the CIP simulated according to the rigorous imaging process of an AOS. In tests, the method achieved a high theoretical accuracy of better than 0.1 pixels.

Cloud detection plays an important role in remote sensing image quality evaluation, information acquisition, and analysis. For most optical satellites, the acquired multispectral bands include only three visible bands and one near-infrared band. How to achieve fast and accurate cloud detection with this limited number of bands is a problem worth studying. In this paper, a cloud detection algorithm (SMFCD) based on a saliency-based mixed feature map of images is proposed. This algorithm first calculates the saliency characteristics from the band mean and haze optimized transformation (HOT) map. The transmission map obtained from dark channel prior theory is used to combine the mixed feature map for subsequent detection. The proposed algorithm generates the initial thick cloud mask using a segmentation method based on the Otsu algorithm later. Guided filtering is then used to refine the cloud mask. The final cloud detection result is acquired through post processing. The algorithm is tested on six different datasets. It is shown that the algorithm can obtain good detection results for most images, with overall accuracies from 0.860 to 0.969 on these datasets. The average time consumption on these datasets reaches 4.4~42.1 seconds, and a fast version of the proposed algorithm reduces these times by about 50%. The proposed algorithm can be used for remote sensing image quality evaluation and subsequent application preprocessing with greater adaptability and flexibility than existing algorithms, since it does not depend on specific satellite radiometric calibration coefficients.