With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined water body extraction from optical panchromatic images often experiences serious under- or over- segmentation problems. In this paper, for producing refined water body extraction results from optical panchromatic images, we propose a fusion feature multi-scale pooling for Markov modeling method. Markov modeling includes two aspects: label field initialization and feature field establishment. These two aspects are jointly created by the fusion feature multi-scale pooling process, and this process is proposed to enhance the feature difference between water bodies and land cover. Then, the greedy algorithm in the iteration conditional method is used to extract refined water bodies according to the rebuilt Markov initial label and feature fields. Finally, to prove the effectiveness of proposed method, extensive experiments were used with collected 2.5m SPOT 5 and 1m GF-2 optical panchromatic images and evaluation indexes (precision, recall, overall accuracy, kappa coefficient and boundary detection ratios) to demonstrate that our proposed method can produce more refined water body extraction results than the state-of-the-art methods. The global and local refined indexes are improved by about 7% and 10%, respectively.

Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis. First, the proposed method can separate a full image into two types of local scenes (i.e., simple or complex local scenes). Next, simple local scenes would utilize the fast saliency model (FSM) to rapidly complete candidate extraction, and for complex local scenes, the ship feature clustering model (SFCM) will be applied to achieve refined detection against severe background interferences. The FSM considers a fusion enhancement image as an input of the pulse response analysis in the frequency domain to achieve rapid ship detection in simple local scenes. Next, the SFCM builds the descriptive model of the ship feature clustering algorithm to ensure the detection performance on complex local scenes. Extensive experiments on SPOT-5 and GF-2 ocean optical remote sensing images show that the proposed ship detection framework has better performance than the state-of-the-art methods, and it addresses the tricky problem of real-time ocean ship detection under strong waves, broken clouds, extensive cloud cover, and ship fleet interferences. Finally, the proposed ocean ship detection framework is demonstrated on an onboard processing hardware.

Referring Remote Sensing Image Segmentation (RRSIS) is a representative multimodal understanding task for remote sensing, which segments designated targets from remote images according to free-form natural language descriptions. However, complex remote sensing characteristics, such as cluttered backgrounds, large-scale variations, small scattered targets and repetitive textures, lead to unstable visual grounding and further spatial grounding drift, resulting in inaccurate segmentation results. Existing approaches typically perform implicit visual–linguistic fusion across encoding and decoding stages, entangling spatial grounding with mask refinement. This tightly coupled formulation lacks explicit structural constraints and is prone to cross-modal ambiguity, especially in complex remote sensing layouts. To address these limitations, we propose a Structurally consistent and Grounding-aware Stagewise Reasoning Framework (SGSRF) that follows a grounding-first, segmentation-second paradigm. The framework decomposes inference into three cascaded stages with progressively imposed structural constraints. First, Cross-modal Consistency Refinement (CCR) lays the foundation for stable spatial grounding by enhancing visual–textual structural alignment via CLIP-based features and Structural Consistency Regularization (SCR), producing well-aligned multimodal representations and reliable grounding cues. Second, Grounding-aware Prompt (GPG) Generation bridges grounding and segmentation by converting aligned representations into complementary sparse and dense prompts, which serve as explicit grounding guidance for the segmentation model. Third, Grounding Modulated Segmentation (GMS) leverages the Segment Anything Model (SAM) to generate fine-grained mask prediction under the joint guidance of prompts and grounding cues, improving spatial grounding stability and robustness to background interference and scale variation. Extensive experiments on three remote sensing benchmarks, namely RefSegRS, RRSIS-D, and RISBench, demonstrate that SGSRF achieves state-of-the-art performance. The proposed stagewise paradigm integrates structural alignment, explicit grounding, and prompt-driven segmentation into a unified framework, providing a practical and robust solution for RRSIS in real-world Earth observation applications.