The fisheye camera, with its large viewing angle, can acquire more spatial information in one shot and is widely used in many fields. However, a fisheye image contains large distortion, resulting in that many scholars have investigated its accuracy of orthorectification, i.e., generation of digital orthophoto map (DOM). This paper presents an orthorectification method, which first determines the transformation relationship between the fisheye image points and the perspective projection points according to the equidistant projection model, i.e., determines the spherical distortion of the fisheye image; then introduces the transformation relationship and the fisheye camera distortion model into the collinearity equation to derive the fisheye image orthorectification model. To verify the proposed method, high accuracy of the fisheye camera 3D calibration field is established to obtain the interior and exterior orientation parameters (IOPs/EOPs) and distortion parameters of the fisheye lens. Three experiments are used to verify the proposed orthorectification method. The root mean square errors (RMSEs) of the three DOMs are averagely 0.003 m, 0.29 m, and 0.61 m, respectively. The experimental results demonstrate that the proposed method is correct and effective.

Edge detection is a crucial task in image processing and remote sensing, particularly for accurately identifying and separating shapes in noisy digital images. To enhance robustness and detail in edge detection, this study presents an innovative edge detection method, which integrates a denoising module and an adaptive thresholding technique to effectively address challenges associated with Gaussian noise in images. The proposed denoising module employs wavelet and Gaussian denoising functions to decompose, filter, and reconstruct the image, thereby reducing the impact of noise and enhancing image quality. For edge detection, an adaptive thresholding method based on a modified OTSU method is utilized. Comprehensive experiments validate the proposed framework by comparing detected edges against ground truth across various levels of Gaussian noise (0.1%, 10%, 20%, and 30%). The median thresholding function is chosen for its stability and convenience, while hard thresholding is avoided due to its tendency to introduce artifacts. Objective metrics, including Mean Squared Error (MSE), Accuracy, and Peak Signal-to-Noise Ratio (PSNR), are employed for evaluation. Comparative results indicate that the proposed method outperforms traditional methods, such as Canny and Roberts, showcasing its effectiveness in edge detection.

Object detection in remote sensing imagery remains challenging due to extreme scale variations, and deployment computational inefficiency. Existing transformer-based detectors exhibit incomplete feature representations stemming from limited scale-awareness in handling multi-scale objects, insufficient modeling of negative-polarity interactions and uniform attention distributions. To address these limitations, this paper proposes a novel framework integrating a Global Edge Information Extraction (GEIE) module and Polarity-aware Linear Attention-based Internal Feature Interaction (PLAIFI) module. The GEIE module consists of two components to integrate multi-scale edge features: a Poly-Resolution Edge Feature Producer (PREFP) module and an Edge Fusion module. The PREFP module extracts edge information using a 3D SobelConv branch applied to shallow features and generates a multi-scale feature map to augment the hybrid encoder’s multi-scale representations with edge-aware priors. The Edge Fusion module supports flexible integration to enrich boundary-aware representations. To establish complementary feature enhancement, the PLAIFI module decomposes query-key pairs into positive/negative polarity components via learnable projection matrices, explicitly models same- and opposite-signed interactions through dual-polarity branches with polarity-sensitive coefficient matrices, and employs channel-wise learnable power functions to sharpen attention distributions by reducing entropy - enhancing scale-awareness while maintaining linear complexity. Extensive experiments demonstrate state-of-the-art performance: 95.0% mAP (+4.6%) on HRSC2016 (small-scale ships), 73.0% mAP (+5.9%) on DOTAv1.5 (multi-class objects), and 93.9% mAP on SIRSTv2 (tiny-scale infrared targets), achieved with 11.4% GFLOPs/11% Params increase over baseline RT-DETR. The principal innovation constitutes a synergistic framework where edge-aware feature recalibration and polarity-driven attention refinement mutually reinforce each other, establishing new state-of-the-art performance while maintaining deployment-friendly computational efficiency on satellite imagery. The source code will be available at https://github.com/Keykeykeykeykeykey/Global_Edge_Guidance.

Interest point detection (IPD) is one of the most fundamental studies in computer vision and other communities. The methods for IPD have evolved from early simple mathematical model-based to current artificial intelligence (AI)-based methods; from early two-dimensional (2D) gray image-based to three-dimensional (3D) point cloud data-based methods, and from early personal computer (PC)-based to Field Programmable Gate Array (FPGA) chip-based onboard methods. Many scholars are very concerning how about these methods’ advantages and disadvantages and what the future development on the IPD method is. Therefore, this paper makes a deep and comprehensive overview for two major types of the IPD methods, the hand-crafted and machine learning, including their advantages, disadvantages, localization accuracy, noise immunity and others, for over 40 years. A comprehensive analysis using average repeatability (AR) and localization error (LE) is also evaluated. The future development for the IPD is finally given.

1 Accurate object detection in optical remote sensing imagery is a critical requirement for applications such as maritime surveillance (Hamzenejadi and Mohseni 2023), urban monitoring (Liu et al. 2...