دانلود کتاب هوش محاسباتی برای تشخیص تغییرات تصویر سنجش از دور

Remote sensing technology enables non-contact, large-scale observation of the Earth’s surface by measuring the reflected or emitted electromagnetic radiation across different spectral bands. It overcomes the limitations of traditional manual observation in timeliness, subjectivity, and spatial coverage, and has become indispensable for disaster management, sustainable development, ecological conservation, and national security. As a core task in remote sensing interpretation, remote sensing image change detection analyzes multi-temporal images to automatically identify temporal evolution patterns in land cover. It provides critical support for dynamic environmental monitoring, post-disaster assessment, and strategic surveillance. 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 _ield, automatically learns hierarchical feature representations from raw data using neural networks inspired by biological perception, unlocking powerful, data-driven representational capabilities that capture complex spatiotemporal 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.