Due to the significant nonlinear intensity changes of multispectral images, automatic image feature point matching is a challenging task. This letter addresses the problem and proposes a novel descriptor combining the structure and texture information to solve the nonlinear intensity variations of multispectral images. We first propose directional maps, i.e., the directional response maps (DMs) and the directional response binary maps (DBMs), which can capture the common structure and texture properties of multispectral images, respectively. We then use the spatial pooling pattern of the histogram of oriented gradients to separately describe the local region of each point of interest based on the DMs and DBMs. In order to speed up the calculation, we apply Gaussian filters to the DMs and average filters to the DBMs to construct the per-pixel histogram bins. Finally, we conjoin the normalized feature vectors corresponding to the structure description and texture description of each point of interest to obtain the histograms of directional maps (HoDMs). The proposed HoDM descriptor was evaluated using three data sets composed of images obtained in both visible light and infrared spectra. The experimental results confirm that the proposed HoDM descriptor is robust to the nonlinear intensity changes of multispectral images and has a superior matching performance as well as a much higher computational efficiency.

Local region description of multi-sensor images remains a challenging task in remote sensing image analysis and applications due to the non-linear radiation variations between images. This paper presents a novel descriptor based on the combination of the magnitude and phase congruency information of local regions to capture the common features of images with non-linear radiation changes. We first propose oriented phase congruency maps (PCMs) and oriented magnitude binary maps (MBMs) using the multi-oriented phase congruency and magnitude information of log-Gabor filters. The two feature vectors are then quickly constructed based on the convolved PCMs and MBMs. Finally, a dense descriptor named the histograms of oriented magnitude and phase congruency (HOMPC) is developed by combining the histograms of oriented phase congruency (HPC) and the histograms of oriented magnitude (HOM) to capture the structure and shape properties of local regions. HOMPC was evaluated with three datasets composed of multi-sensor remote sensing images obtained from unmanned ground vehicle, unmanned aerial vehicle, and satellite platforms. The descriptor performance was evaluated by recall, precision, F1-measure, and area under the precision-recall curve. The experimental results showed the advantages of the HOM and HPC combination and confirmed that HOMPC is far superior to the current state-of-the-art local feature descriptors.

Dense stereo matching based on two viewpoint images is an important step in 3D reconstruction. In this study, a new disparity estimation algorithm for depth discontinuity areas is proposed. Dense stereo matching is implemented on the basis of the semi-global matching algorithm. If the ground truth is known, then mismatched pixels are selected as those with a difference of more than one pixel from the ground truth. Otherwise, the left-right consistency checking process is used. Then, the Canny operator is used to detect image edges. For each mismatched pixel, a maximum total of eight nearest edge points exists in four pairs of symmetric directions. Basically, the process of searching for the nearest valid point proceeds along directions in the following sequence: horizontal, vertical, 45° oblique and 135° oblique. Specifically, for each pair of symmetric directions, the nearest valid pixel is searched along the direction of the distant edge point. If the disparity is successfully reassigned by the nearest valid pixel found, the searching process is terminated. Comparative results obtained using well-known benchmark datasets and unmanned aerial vehicle stereo images demonstrate the excellent performance of the proposed method.

As a fundamental problem, spatial resection based on collinearity equations has been a subject of study since the beginning of photogrammetry. Owing to the nonlinearity of collinearity equations, the standard Newton-Raphson method requires approximate values for unknown parameters. Therefore, the method is inapplicable to modern oblique photogrammetry. In this letter, we propose a new altitude spatial resection algorithm that combines closed-form solutions and iterative solutions. In this algorithm, a set of homotopy algorithm solutions is first selected as the initial value on the basis of the minimum sum of the squared errors. Quasi-Newton method is then used for nonlinear iteration. Experimental results obtained from different data sets indicate excellent performance compared with serial implement.

Inclined imaging is an advanced sensing technique, and has been extensively used. Therefore, large-rotation angle photogrammetric resection has become an important topic. However, traditional iterative methods are limited by requiring good initial values. By contrast, noniterative methods do not require an initial value, although they exhibit relatively low accuracy and robustness. To obtain results with superior precision and universality, this letter proposes an improved approach by modifying the initial value acquisition and iterative methods. This algorithm uses nonlinear iteration to reduce the model error, thereby possibly achieving an exceptional convergence for large-rotation-angle photogrammetric resection. Experimental results on the real data indicate that the proposed algorithm outperforms the previous methods.