Object detection from high-resolution aerial images has received increasing attention during the last few years. It is a common practice to downsize images before feeding them into a network. In real life, there are lots of scenes where many objects gather together in certain areas, such as crossroads, parking lots, and playgrounds. The downsizing operation significantly limits the detection ability in these scenes. In this article, we proposed an unsupervised cluster guided detection framework (UCGNet) to address these issues by guiding the detector focus on the object-densely distributed area. In particular, a local location module is first applied to predict a binary map presenting how objects distribute in terms of the pixel of the map. Then, an unsupervised cluster method is used to produce dense regions. Each adjusted dense region is fed into the detector for object detection. Finally, a global merge module generates the final predict results. Experiments were conducted on two popular aerial image datasets including VisDrone2019 and UAVDT. In both datasets, our proposed method outperforms the existing baseline methods with achieving 32.8% and 19.1% mAP, respectively.

When the number of available training views is limited, NeRF and 3DGS will soon overfit the optimization and learn the wrong scene geometry. For this challenge, a common solution is to provide depth prior as supervision to correct scene geometry. In this work, we present Geometric Regularized 3D Gaussian Splatting (GeoRGS), a priors-independent method for improving novel view synthesis from sparse inputs. We analyze the problems of the density control strategy in 3DGS with sparse inputs, and find that correcting the erroneous Gaussian growth trend at the beginning of training is effective in mitigating overfitting. Based on this analysis, we propose two geometric regularization methods that do not require prior information. One is based on selecting seed patches of 3D Gaussian from the scene, which guides growth to form correct scene geometry, while the other focuses on regularizing depth similarity between object surfaces and edges. GeoRGS achieves state-of-the-art performance in novel view synthesis from sparse input on LLFF, Blender, RealEstate10K and MipNeRF360 datasets, while also demonstrating significantly faster training speeds and rendering efficiency compared to other baselines.

3-D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3-D object detection. We propose a novel flexible 3-D heatmap auxiliary network (3-D HANet) for object detection. To obtain complete structure and location information from an incomplete point cloud structure, we propose a 3-D heatmap to reflect object information. Also, we design a plug-and-play auxiliary network based on 3-D heatmap, which improves the accuracy of the entire detection network without extra computation in the inference stage. We validate the 3-D HANet on the basis of three classic 3-D object detection networks: PointPillars, sparsely embedded convolutional detection (SECOND), and structure aware single-stage 3-D object detection from point cloud (SASSD). Experimental results show that our auxiliary network augments the feature extraction ability of the backbone network, which is manifested in that the predicted boxes and the ground-truth boxes are more suitable in size and more aligned in direction. Furthermore, we conducted verification experiments on the state-of-the-art (SOTA) detector, CasA, and made a further improvement on the official ranking of the KITTI dataset.

Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. In this paper, to make full use of HSI information, we combined spectral and spatial information into a two-dimension image in a particular order by extracting a data cube and unfolding it. Prior to the step of combining, principle component analysis (PCA) is utilized to decrease the dimensions of HSI so as to reduce computational cost. Moreover, the classification block used during the experiment is a convolutional neural network (CNN). Instead of using traditionally fixed-size kernels in CNN, we leverage a multi-scale kernel in the first convolutional layer so that it can scale to the receptive field. To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. Extensive experiments on the datasets from Pavia University and Salinas demonstrate that the proposed method significantly improves the accuracy in HSI classification.

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data.

Automatic object detection in aerial imagery is being increasingly adopted in many applications, such as traffic monitoring, smart cities, and disaster assistance. In keypoint-based detectors, the prediction modules are usually generated from a fixed feature map scale. This configuration significantly limits the ability to detect multiscale objects in aerial scenes. The corner selection module in these detectors often ignores that a category in an aerial image is relatively unitary. In this article, a novel network, called the multiscale keypoint detection network (MKD-Net), is proposed to address these challenges. MKD-Net fuses multiscale layers to generate multiple feature maps for objects of different sizes. During the inference phase, both feature maps can be exploited for predicting corners. Moreover, a category attention module is designed to reduce the channel noise for a single-category scene. Experiments on benchmarks PASCAL VOC and DOTA show promising performance of MKD-Net compared with the baseline network. The code is available on https://github.com/jason-su/MKD-NET.