Genetic Algorithm and Greedy Strategy-Based Multi-Mission-Point Route Planning for Heavy-Duty Semi-Rigid Airship.

The large volume and windward area of the heavy-duty semi-rigid airship (HSA) result in a large turning radius when the HSA passes through every mission point. In this study, a multi-mission-point route planning method for HSA based on the genetic algorithm and greedy strategy is proposed to direct the HSA maneuver through every mission point along the optimal route. Firstly, according to the minimum flight speed and the maximum turning slope angle of the HSA during turning, the minimum turning radius of the HSA near each mission point is determined. Secondly, the genetic algorithm is used to determine the optimal flight sequence of the HSA from the take-off point through all the mission points to the landing point. Thirdly, based on the optimal flight sequence, the shortest route between every two adjacent mission points is obtained by using the route planning method based on the greedy strategy. By determining the optimal flight sequence and the shortest route, the optimal route for the HSA to pass through all mission points can be obtained. The experimental results show that the method proposed in this study can generate the optimal route with various conditions of the mission points using simulation studies. This method reduces the total voyage distance of the optimal route by 18.60% on average and improves the flight efficiency of the HSA.

Route-Planning Method for Plant Protection Rotor Drones in Convex Polygon Regions.

Aiming at the problem of low operating efficiency due to the poor endurance of plant protection rotor drones and the small volume of pesticide carried, this paper proposes a route-planning algorithm for convex polygon regions based on the initial heading angle. First, a series of coordinate conversion methods ranging from the Earth coordinate system to the local plane coordinate system are studied. Second, in the local plane coordinate system, a route generation method based on subregion is proposed; therefore, multiple routes can be generated with different initial heading angles. Lastly, the optimal route and the best initial heading angle can be obtained after the comparison according to the three evaluation criteria: number of turns, route distance, and pesticide waste rate. The simulation results show that, compared with the common grid method, the route generation method based on subregion reduces the route distance and pesticide waste rate by 2.27% and 13.75%, respectively. Furthermore, it also shows that, compared with the route generated by the initial heading angle of 0°, the optimal route reduces the number of turns, route distance, and pesticide waste rate by 60%, 17.65%, and 38.18%, respectively. The route was optimized in three aspects and reached the best overall result using this method, which in turn proved its feasibility.

Continuous-Time Laser Frames Associating and Mapping via Multilayer Optimization.

To achieve the ability of associating continuous-time laser frames is of vital importance but challenging for hand-held or backpack simultaneous localization and mapping (SLAM). In this study, the complex associating and mapping problem is investigated and modeled as a multilayer optimization problem to realize low drift localization and point cloud map reconstruction without the assistance of the GNSS/INS navigation systems. 3D point clouds are aligned among consecutive frames, submaps, and closed-loop frames using the normal distributions transform (NDT) algorithm and the iterative closest point (ICP) algorithm. The ground points are extracted automatically, while the non-ground points are automatically segmented to different point clusters with some noise point clusters omitted before 3D point clouds are aligned. Through the three levels of interframe association, submap matching and closed-loop optimization, the continuous-time laser frames can be accurately associated to guarantee the consistency of 3D point cloud map. Finally, the proposed method was evaluated in different scenarios, the experimental results showed that the proposed method could not only achieve accurate mapping even in the complex scenes, but also successfully handle sparse laser frames well, which is critical for the scanners such as the new Velodyne VLP-16 scanner's performance.

Hyperspectral Imagery Super-Resolution by Adaptive POCS and Blur Metric.

The spatial resolution of a hyperspectral image is often coarse as the limitations on the imaging hardware. A novel super-resolution reconstruction algorithm for hyperspectral imagery (HSI) via adaptive projection onto convex sets and image blur metric (APOCS-BM) is proposed in this paper to solve these problems. Firstly, a no-reference image blur metric assessment method based on Gabor wavelet transform is utilized to obtain the blur metric of the low-resolution (LR) image. Then, the bound used in the APOCS is automatically calculated via LR image blur metric. Finally, the high-resolution (HR) image is reconstructed by the APOCS method. With the contribution of APOCS and image blur metric, the fixed bound problem in POCS is solved, and the image blur information is utilized during the reconstruction of HR image, which effectively enhances the spatial-spectral information and improves the reconstruction accuracy. The experimental results for the PaviaU, PaviaC and Jinyin Tan datasets indicate that the proposed method not only enhances the spatial resolution, but also preserves HSI spectral information well.

Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems.

Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted "useful" data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

[Hyperspectral Band Selection Based on Spectral Clustering and Inter-Class Separability Factor].

With the development of remote sensing technology and imaging spectrometer, the resolution of hyperspectral remote sensing image has been continually improved, its vast amount of data not only improves the ability of the remote sensing detection but also brings great difficulties for analyzing and processing at the same time. Band selection of hyperspectral imagery can effectively reduce data redundancy and improve classification accuracy and efficiency. So how to select the optimum band combination from hundreds of bands of hyperspectral images is a key issue. In order to solve these problems, we use spectral clustering algorithm based on graph theory. Firstly, taking of the original hyperspectral image bands as data points to be clustered , mutual information between every two bands is calculated to generate the similarity matrix. Then according to the graph partition theory, spectral decomposition of the non-normalized Laplacian matrix generated by the similarity matrix is used to get the clusters, which the similarity between is small and the similarity within is large. In order to achieve the purpose of dimensionality reduction, the inter-class separability factor of feature types on each band is calculated, which is as the reference index to choose the representative bands in the clusters furthermore. Finally, the support vector machine and minimum distance classification methods are employed to classify the hyperspectral image after band selection. The method in this paper is different from the traditional unsupervised clustering method, we employ spectral clustering algorithm based on graph theory and compute the interclass separability factor based on a priori knowledge to select bands. Comparing with traditional adaptive band selection algorithm and band index based on automatically subspace divided algorithm, the two sets of experiments results show that the overall accuracy of SVM is about 94. 08% and 94. 24% and the overall accuracy of MDC is about 87. 98% and 89. 09%, when the band selection achieves a relatively optimal number of clusters using the method propoesd in this paper. It effectively remains spectral information and improves the classification accuracy.

A Multispectral Image Creating Method for a New Airborne Four-Camera System with Different Bandpass Filters.

This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.

[The linear hyperspectral camera rotating scan imaging geometric correction based on the precise spectral sampling].

As the rotation speed of ground based hyperspectral imaging system is too fast in the image collection process, which exceeds the speed limitation, there is data missed in the rectified image, it shows as the_black lines. At the same time, there is serious distortion in the collected raw images, which effects the feature information classification and identification. To solve these problems, in this paper, we introduce the each component of the ground based hyperspectral imaging system at first, and give the general process of data collection. The rotation speed is controlled in data collection process, according to the image cover area of each frame and the image collection speed of the ground based hyperspectral imaging system, And then the spatial orientation model is deduced in detail combining with the star scanning angle, stop scanning angle and the minimum distance between the sensor and the scanned object etc. The oriented image is divided into grids and resampled with new spectral. The general flow of distortion image corrected is presented in this paper. Since the image spatial resolution is different between the adjacent frames, and in order to keep the highest image resolution of corrected image, the minimum ground sampling distance is employed as the grid unit to divide the geo-referenced image. Taking the spectral distortion into account caused by direct sampling method when the new uniform grids and the old uneven grids are superimposed to take the pixel value, the precise spectral sampling method based on the position distribution is proposed. The distortion image collected in Lao Si Cheng ruin which is in the Zhang Jiajie town Hunan province is corrected through the algorithm proposed on above. The features keep the original geometric characteristics. It verifies the validity of the algorithm. And we extract the spectral of different features to compute the correlation coefficient. The results show that the improved spectral sampling method is better than the direct sampling method. It provides the reference for the similar product used on the ground.

[The hyperspectral camera side-scan geometric imaging in any direction considering the spectral mixing].

In order to correct the image distortion in the hyperspectral camera side-scan geometric Imaging, the image pixel geo-referenced algorithm was deduced in detail in the present paper, which is suitable to the linear push-broom camera side-scan imaging on the ground in any direction. It takes the orientation of objects in the navigation coordinates system into account. Combined with the ground sampling distance of geo-referenced image and the area of push broom imaging, the general process of geo-referenced image divided into grids is also presented. The new image rows and columns will be got through the geo-referenced image area dividing the ground sampling distance. Considering the error produced by round rule in the pixel grids generated progress, and the spectral mixing problem caused by traditional direct spectral sampling method in the process of image correction, the improved spectral sampling method based on the weighted fusion method was proposed. It takes the area proportion of adjacent pixels in the new generated pixel as coefficient and then the coefficients are normalized to avoid the spectral overflow. So the new generated pixel is combined with the geo-referenced adjacent pixels spectral. Finally the amounts of push-broom imaging experiments were taken on the ground, and the distortion images were corrected according to the algorithm proposed above. The results show that the linear image distortion correction algorithm is valid and robust. At the same time, multiple samples were selected in the corrected images to verify the spectral data. The results indicate that the improved spectral sampling method is better than the direct spectral sampling algorithm. It provides reference for the application of similar productions on the ground.

[An improved low spectral distortion PCA fusion method].

Aiming at the spectral distortion produced in PCA fusion process, the present paper proposes an improved low spectral distortion PCA fusion method. This method uses NCUT (normalized cut) image segmentation algorithm to make a complex hyperspectral remote sensing image into multiple sub-images for increasing the separability of samples, which can weaken the spectral distortions of traditional PCA fusion; Pixels similarity weighting matrix and masks were produced by using graph theory and clustering theory. These masks are used to cut the hyperspectral image and high-resolution image into some sub-region objects. All corresponding sub-region objects between the hyperspectral image and high-resolution image are fused by using PCA method, and all sub-regional integration results are spliced together to produce a new image. In the experiment, Hyperion hyperspectral data and Rapid Eye data were used. And the experiment result shows that the proposed method has the same ability to enhance spatial resolution and greater ability to improve spectral fidelity performance.