Computational Intelligence for Remote Sensing Image by Jiao Shi(.PDF)+
File Size: 61.7 MB
Computational Intelligence for Remote Sensing Image Change Detection (SpringerBriefs in Computer Science) by Jiao Shi, Yu Lei, Maoguo Gong, Nan Zhang
Requirements: .ePUB, .PDF reader, 61.7 MB | True PDF, True EPUB
Overview: Nowadays, remote sensing systems and technologies have been widely studied and applied in environmental monitoring, land survey, and disaster management. As a pivotal remote sensing task, change detection aims to identify and quantify spatio-temporal changes using multi-temporal imagery, supporting timely decision-making and sustainable resource planning. Nevertheless, conventional change detection approaches remain limited in addressing challenges including sensitivity to noise, discrepancies in spatial resolution, sensor misalignment, and the fusion of multi-source heterogeneous data. To address these issues, advanced Computational Intelligence (CI) techniques, particularly Deep Learning and evolutionary computation, are being increasingly adopted, offering improved robustness and adaptability for modern change detection tasks. Traditional change detection methods rely on manual features and statistical modeling to discriminate changes in multi-temporal remote sensing data. However, under diverse land cover, radiometric inconsistencies, and noise interference, they often suffer from error propagation, limited representational capacity, and poor generalization across complex or heterogeneous scenes. To address these issues, computational intelligence has emerged as a powerful alternative to conventional approaches. Deep Learning, a major branch of this field, automatically learns hierarchical feature representations from raw data using neural networks inspired by biological perception, unlocking powerful, data-driven representational capabilities that capture complex spatio-temporal dynamics in remote sensing imagery. More recently, neural architecture search has been adopted to automate the design of efficient and task-adapted network architectures, thereby further pushing the performance boundaries beyond those achievable with manually designed architectures. These paradigms can offer effective solutions to the fundamental shortcomings of traditional change detection methods.
Genre: Non-Fiction > Tech & Devices
Free Download links: