Semantic segmentation methods based on deep learning considerably improve the segmentation performance of remote sensing images. However, with the extensive application of high-resolution remote sensing images, additional details introduce considerable interference to the learning process for classification, thereby diminishing the accuracy of segmentation and resulting in blurry object boundaries. To address this problem, this study designed Random-Walk-SegNet (RWSNet), a semantic segmentation network based on SegNet combined with random walk. First, SegNet is used as the basic architecture with the sliding window strategy that optimizes the network output to improve the continuity and smoothness of segmentation. Second, seed regions of the random walk are selected in accordance with the classification output of SegNet. Third, the weights of the undirected graph edge are determined by fusing the gradient of the original image and probability map of SegNet. Finally, random walk is implemented on the entire image, thus reducing edge blur and realizing high-performance semantic segmentation of remote sensing images. In comparison with mainstream and other improved methods, the proposed network has lower complexity but better performance, and the algorithm is state-of-the-art and robust.

Events-to-video (E2V) reconstruction and video-to-events (V2E) simulation are two fundamental research topics in event-based vision. Current deep neural networks for E2V reconstruction are usually complex and difficult to interpret. Moreover, existing event simulators are designed to generate realistic events, but research on how to improve the event generation process has been so far limited. In this paper, we propose a light, simple model-based deep network for E2V reconstruction, explore the diversity for adjacent pixels in V2E generation, and finally build a video-to-events-to-video (V2E2V) architecture to validate how alternative event generation strategies improve video reconstruction. For the E2V reconstruction, we model the relationship between events and intensity using sparse representation models. A convolutional ISTA network (CISTA) is then designed using the algorithm unfolding strategy. Long short-term temporal consistency (LSTC) constraints are further introduced to enhance the temporal coherence. In the V2E generation, we introduce the idea of having interleaved pixels with different contrast threshold and lowpass bandwidth and conjecture that this can help extract more useful information from intensity. Finally, V2E2V architecture is used to verify the effectiveness of this strategy. Results highlight that our CISTA-LSTC network outperforms state-of-the-art methods and achieves better temporal consistency. Sensing diversity in event generation reveals more fine details and this leads to a significantly improved reconstruction quality.

Image registration is an important step in remote sensing image processing, especially for images of urban areas, which are often used for urban planning, environmental assessment, and change detection. Urban areas have many artificial objects whose contours and edges provide abundant line features. However, the locations of line endpoints are greatly affected by large background variations. Considering that line intersections remain relatively stable and have high positioning accuracy even with large background variations, this paper proposes a high-accuracy remote sensing image registration algorithm that is based on the line-intersection-line (LIL) structure, with two line segments and their intersection. A double-rectangular local descriptor and a spatial relationship-based outlier removal strategy are designed on the basis of the LIL structure. First, the LILs are extracted based on multi-scale line segments. Second, LIL local descriptors are built with pixel gradients in the LIL neighborhood to realize initial matching. Third, the spatial relations between initial matches are described with the LIL structure and simple affine properties. Finally, the graph-based LIL outlier removal strategy is conducted and incorrect matches are eliminated step by step. The proposed algorithm is tested on simulated and real images and compared with state-of-the-art methods. The experiments prove that the proposed algorithm can achieve sub-pixel registration accuracy, high precision, and robust performance even with significant background variations.

First spike timings are crucial for decision-making in spiking neural networks (SNNs). A recently introduced first-spike (FS) coding method demonstrates comparable accuracy to firing-rate (FR) coding in processing complex temporal information through supervised learning. However, its performance still falls behind advanced approaches. In order to explore the potential of FS coding, we enhance the capability of SNNs in classifying auditory datasets by improving neural dynamics. We propose a current-based adaptive LIF neuron (CuAdLIF) with delayed responses and membrane potential adaptation to enhance temporal correlations and preserve long short-memory. Furthermore, we introduce strategies to minimize delays in decision-making and enable adaptive training for FS coding. Results show that the CuAdLIF neuron enhances the extraction of temporal features and significantly improves FS coding accuracy. In addition, our strategies effectively reduce output time delays.

In this paper, in view of the grayscale information and morphological changes of remote sensing images, a registration algorithm based on the distribution information of the global edge and the local information of the feature points is proposed. Lifting wavelet transform is used for multi-scale edge extraction of stable global edge, such as river and coastline. On the basis of SIFT feature point detection, the Equivalent DoG (EDoG) pyramid is constructed and the scale-invariant feature points on edge are extracted. The SIFT descriptor is combined with the global descriptor to construct Global-SIFT (G-SIFT) descriptor. The algorithm is tested on the remote sensing images before and after severe natural disasters such as earthquakes, floods and storms. Compared with the performance of SIFT algorithm, the algorithm has advantages in terms of algorithm stability, matching accuracy and registration precision. It can meet the registration accuracy requirement in remote sensing image with large changes.

RGB-Event tracking has become a promising trend in visual object tracking to leverage the complementary strengths of both RGB images and dynamic spike events for improved performance. However, existing artificial neural networks (ANNs) struggle to fully exploit the sparse and asynchronous nature of event streams. Recent efforts toward hybrid architectures combining ANNs and spiking neural networks (SNNs) have emerged as a promising solution in RGB-Event perception, yet effectively fusing features across heterogeneous paradigms remains a challenge. In this work, we propose ISTASTrack, the first transformer-based ANN-SNN hybrid Tracker equipped with ISTA adapters for RGB-Event tracking. The two-branch model employs a vision transformer to extract spatial context from RGB inputs and a spiking transformer to capture spatio-temporal dynamics from event streams. To bridge the modality and paradigm gap between ANN and SNN features, we systematically design an ISTA adapter for bidirectional feature interaction between the two branches. The ISTA adapter is derived from the sparse representation theory by unfolding the iterative shrinkage-thresholding algorithm. Additionally, we incorporate a temporal downsampling attention module within the adapter to align multi-step SNN features with single-step ANN features in the latent space. Experimental results on RGB-Event tracking benchmarks, such as FE240hz, VisEvent, COESOT, and FELT, have demonstrated that ISTASTrack achieves state-of-the-art performance while maintaining high energy efficiency. This work highlights the effectiveness and practicality of hybrid ANN-SNN designs for robust visual tracking. The code is publicly available at https://github.com/lsying009/ISTASTrack.git.