Hyperspectral (HS) video is able to capture abundant spectral, spatial, and temporal information of objects, which overcomes the limitations of common red-green-blue (RGB) video in complex scenarios such as similar appearances and background clutters. However, most of trackers apply hand-crafted features extracted from manually selected bands instead of deep features for object representations due to limited HS data and the band gap problem. Each HS image consists of many bands, and it is challenging to fully interact with the band information while maintaining tracking speed. To this end, this paper proposes a novel end-to-end spectral awareness interaction network with dynamic template (SPIRIT) for HS video object tracking. Firstly, a spectral awareness module is proposed to learn band contributions with consideration of nonlinear and global interactions between HS bands. It can also cooperate with the feature extraction module pre-trained with RGB data to attenuate the band gap and data-hungry. Secondly, an interaction module is proposed to achieve inter- and intra-band feature interactions to enhance tracking performance while improving efficiency. Furthermore, the proposed method contains a novel update module that evaluates the tracking confidence of the current state to adapt to object changes and attenuate tracking drifts. Extensive experiments demonstrate the superiority of our approach compared to state-of-the-arts while meeting real-time demands.

Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km² and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method.

High-level feature extraction and hierarchical feature representation of image objects with a convolutional neural network (CNN) can overcome the limitations of the traditional building detection models using middle/low-level features extracted from a complex background. Aiming at the drawbacks of manual village location, high cost, and the limited accuracy of building detection in the existing rural building detection models, a two-stage CNN model is proposed in this letter to detect rural buildings in high-resolution imagery. Simulating the hierarchical processing mechanism of human vision, the proposed model is constructed with two CNNs, whose architectures can automatically locate villages and efficiently detect buildings, respectively. This two-stage CNN model effectively reduces the complexity of the background and improves the efficiency of rural building detection. The experiments showed that the proposed model could automatically locate all the villages in the two study areas, achieving a building detection accuracy of 88%. Compared with the existing models, the proposed model was proved to be effective in detecting buildings in rural areas with a complex background.

Satellite video (SV) can acquire rich spatiotemporal information on the earth. Single object tracking (SOT) in SVs enables the continuous acquisition of the position and range of a specific object, expanding the field of remote-sensing applications. In SVs, objects are small with limited features and vulnerable to tracking drift. In this article, a correlation filter based dual-flow (DF) tracker is proposed to explore how the hybridization of spatial–spectral feature fusion and motion model can boost tracking. To represent small objects, the DF adaptively fuses complementary features using a state-aware indicator in feature flow. In motion flow, the indicator perceives the confidence of the feature flow. A dual-mode prediction model is then constructed to simulate the object's motion pattern and cooperate linear and nonlinear motion patterns to implement SOT in SVs. The ablation experiments demonstrate that the DF contributes to tracking. Experimental comparisons on 14 real SVs captured by the Jilin-1 satellite constellation show that DF achieves optimal performance with an area under the curve of 0.912 in the precision plot, 0.700 in the success plot, and a speed of 155.2 frames per second. This work would encourage the development of remote-sensing ground surveillance.