Object detection is a basic issue of very high-resolution remote sensing images (RSIs) for automatically labeling objects. At present, deep learning has gradually gained the competitive advantage for remote sensing object detection, especially based on convolutional neural networks (CNNs). Most of the existing methods use the global information in the fully connected feature vector and ignore the local information in the convolutional feature cubes. However, the local information can provide spatial information, which is helpful for accurate localization. In addition, there are variable factors, such as rotation and scaling, which affect the object detection accuracy in RSIs. In order to solve these problems, this paper presents a hierarchical robust CNN. First, multiscale convolutional features are extracted to represent the hierarchical spatial semantic information. Second, multiple fully connected layer features are stacked together so as to improve the rotation and scaling robustness. Experiments on two data sets have shown the effectiveness of our method. In addition, a large-scale high-resolution remote sensing object detection data set is established to make up for the current situation that the existing data set is insufficient or too small. The data set is available at https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset.

In the multicategory object detection task of high-resolution remote sensing images, small objects are always difficult to detect. This happens because the influence of location deviation on small object detection is greater than on large object detection. The reason is that, with the same intersection decrease between a predicted box and a true box, Intersection over Union (IoU) of small objects drops more than those of large objects. In order to address this challenge, we propose a new localization model to improve the location accuracy of small objects. This model is composed of two parts. First, a global feature gating process is proposed to implement a channel attention mechanism on local feature learning. This process takes full advantages of global features' abundant semantics and local features' spatial details. In this case, more effective information is selected for small object detection. Second, an axis-concentrated prediction (ACP) process is adopted to project convolutional feature maps into different spatial directions, so as to avoid interference between coordinate axes and improve the location accuracy. Then, coordinate prediction is implemented with a regression layer using the learned object representation. In our experiments, we explore the relationship between the detection accuracy and the object scale, and the results show that the performance improvements of small objects are distinct using our method. Compared with the classical deep learning detection models, the proposed gated axis-concentrated localization network (GACL Net) has the characteristic of focusing on small objects.

Remote sensing image (RSI) classification is one of the most important fields in RSI processing. It is well known that RSIs are very complicated due to its various kinds of contents. Therefore, it is very difficult to distinguish different scene categories with similar visual contents, like desert and bare land. To address hard negative categories, an attribute-cooperated convolutional neural network (ACCNN) is proposed to exploit attributes as additional guiding information. First, the classification branch extracts convolutional neural network feature, which is then utilized to recognize the RSI scene categories. Second, the attribute branch is proposed to make the network distinguish scene categories efficiently. The proposed attribute branch shares feature extraction layers with the classification branch and makes the classification branch aware of extra attribute information. Finally, the relationship branch constraints the relationship between the classification branch and the attribute branch. To exploit the attribute information, three attribute-classification data sets are generated (AC-AID, AC-UCM, and AC-Sydney). Experimental results show that the proposed method is competitive to state-of-the-art methods. The data sets are available at https://github.com/CrazyStoneonRoad/Attribute-Cooperated-Classification-Data sets.

In remotely sensed images, it is quite common to run into small objects, such as cars and small storage tanks. However, these small objects are quite easy to get ignored because of the positioning difficulty. Thus, small objects detection is very challenging for the remote sensing object detection task. In order to deal with this challenge, the optimized low coupling network (OLCN) is proposed. First, a low coupling robust regression (LCRR) module improves the positioning accuracy to avoid small objects getting missed. Second, a receptive field optimizing layer (RFOL) is proposed to train better classifiers by providing more accurate regions of interest (RoIs). Experimental results on the public dataset HRRSD verify the effectiveness of the proposed OLCN. Small objects detection metric is improved from 5.70% of the baseline to 22.90% of the OLCN. Moreover, the proposed method has reached state-of-the-art performance on the HRRSD dataset.

The Building Change Detection (BCD) task serves urban planning through monitoring the landuse. However, due to the complexity of remote sensing images and high foreground-background similarity, it leads to inaccurate detection of building edge regions. Existing methods deal with this problem by fusing features of different layers. But the fusing operation cannot separate details information from overall information of buildings, resulting in inaccurate detection of building edge area. To address the above challenges, we propose an Iterative Edge Enhancing Framework (IEEF). The IEEF alleviates the building edge detection difficulty by densely implementing Detail Semantic Enhancement Module in decoding part. This module takes differential features between adjacent scales to explicitly represent the building edge information. Simultaneously, to deal with the classes imbalance problem, a Density-Guided Sampling method dedicated for change detection is proposed to increase the proportion of positive samples during training. Our proposed method achieves state-of-the-art performance on the LEVIR-CD dataset and the WHU dataset, and obtains accurate changed building edges.