Aims and Scope

IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications.

Principal topics include:

    Generation and Display: Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality.
    Processing and Analysis: Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing.
    Implementations and Architectures: Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing.
    Coding and Transmission: Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video.
    Retrieval and Multimedia: Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography.
    Applications: Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security.

Special Issue on Computer Vision for Earth Observation and Environmental Monitoring
截稿日期: 2026-04-01

Recent advances in machine vision have revolutionized the ability to process and interpret complex visual data and provide critical insights into Earth observations and environmental monitoring. Examples include novel deep learning methods to identify climate change impacts, monitor biodiversity loss, detect marine pollution, and manage natural disasters. Machine vision technologies are increasingly crucial for their unique transformative solutions to the Earth observation and machine learning fields.

This special issue thus aims to showcase the state-of-the-art machine vision methods and their innovative applications in these multidisciplinary scientific areas. Topics of interest include, but are not limited to, image and video processing algorithms, deep learning techniques for remote sensing data, multispectral and hyperspectral imaging, multi-modality data processing, and the integration of computer vision with other data sources for enhanced decision-making. Submissions focusing on novel algorithms, implementations, architectures, and scalable solutions around this theme addressing real-world challenges are particularly encouraged. By bringing together contributions from leading researchers and practitioners, this special issue seeks to advance the state-of-the-art in machine vision and promote interdisciplinary approaches that bridge technology innovation and environmental sciences.

Topics of interest for this call for papers include but are not restricted to:

    Novel theories, concepts, and models of computer vision technologies for land, ocean, atmosphere and space imagery;
    Advanced methodologies in machine vision for Earth observation and environment observation;
    Hardware/software co-design of machine vision technologies for Earth observations and environmental monitoring;
    Evaluation criteria for the performance in terms of memory, computation and power of machine vision technologies for Earth observations and environmental monitoring;
    Novel applications of machine vision technologies for Earth observations and environmental monitoring such as land use and change, marine aquatic environment monitoring, biodiversity assessment, climate change impacts, and natural disaster monitoring.

Guest Editors:

Prof. Chunbo Luo (Lead)
University of Exeter,
United Kingdom

Dr. Keiller Nogueira
University of Liverpool,
United Kingdom

Dr. Paolo Russo
Sapienza University of Rome,
Italy

Dr. Fabiana Di Ciaccio
University of Florence,
Italy

Prof. Chao Zhou
China University of Geosciences,
China