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With the emergence of novel data collection platforms driven by the Internet of Things (IoT), this study expands beyond the conventional boundaries of traditional remote sensing technologies. The deep integration of remote sensing and computer vision has transitioned into industrial applications, enabling the utilization of Artificial Intelligence to address challenges like automated information extraction and image analysis. Nonetheless, due to the intricate architecture of IoT and the absence of standardized security protocols, remote sensing instruments are highly susceptible to privacy breaches during data transmission. Because traditional cryptographic approaches depend heavily on computational complexity, it is critical to develop security frameworks tailored for resource-constrained IoT devices. Visual Cryptography (VC) serves as an image-based threshold technique that allows direct decoding by the human visual system simply by overlaying encrypted shares. This "stacking-to-see" attribute, combined with basic Boolean decryption processes, positions VC as an optimal candidate for privacy-preserving identification within extensive IoT-based remote sensing imagery. In this investigation, the secure and productive dissemination of high-resolution remote sensing imagery is accomplished via meaningful VC. By distributing the error between the encryption block and the source block to neighboring blocks, the fidelity degradation in the reconstructed images is minimized. Furthermore, by adjusting a pre-trained model using large-scale datasets, the recognition accuracy of small, encrypted remote sensing datasets is enhanced. The empirical findings demonstrate that the introduced lightweight, privacy-preserving recognition system maintains superior identification performance while strengthening data security.