Land-use classification from remote sensing images has become an important but challenging task. This paper proposes Hierarchical Coding Vectors (HCV), a novel representation based on hierarchically coding structures, for scene level land-use classification. We stack multiple Bag of Visual Words (BOVW) coding layers and one Fisher coding layer to develop the hierarchical feature learning structure. In BOVW coding layers, we extract local descriptors from a geographical image with densely sampled interest points, and encode them using soft assignment (SA). The Fisher coding layer encodes those semi-local features with Fisher vectors (FV) and aggregates them to develop a final global representation. The graphical semantic information is refined by feeding the output of one layer into the next computation layer. HCV describes the geographical images through a high-level representation of richer semantic information by using a hierarchical coding structure. The experimental results on the 21-Class Land Use (LU) and RSSCN7 image databases indicate the effectiveness of the proposed HCV. Combined with the standard FV, our method (FV + HCV) achieves superior performance compared to the state-of-the-art methods on the two databases, obtaining the average classification accuracy of 91.5% on the LU database and 86.4% on the RSSCN7 database.

Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets.

With the development of very high resolution satellite image acquisition technology, remote sensing scene classification has become an important and challenging task. In this article, aiming at tackling this task, we propose a hybrid architecture, i.e., aggregated deep Fisher feature (ADFF), which can make full use of deep convolutional features’ rich semantic information and unsupervised encoding's high robustness. Unlike the previous methods, we first explore the optimal encoding layer in the pretraining CNN model, which naturally fuses the local and global image information in a novel way, making the ability of semantic acquisition further enhanced. ADFF can learn more suitable internal features from the remote sensing data, boosting the final performance. We evaluate our algorithm based on several public datasets, and the results show that our approach achieves superior performance compared with the state-of-the-art methods. The proposed ADFF obtains average classification accuracy of 98.81%, 95.21%, 86.01%, and 88.79%, respectively, on the UC Merced Land-Use, RSSCN7, NWPU-RESISC45 (10% for training), and NWPU-RESISC45 (20% for training) datasets.

In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots.

The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function’s challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.