System and method for a multi-scale adaptive scale-space pyramid network for aerial scenes recognition

Abstract

A remote sensing system for performing a method of real-time interpretation of aerial images includes remote imaging platforms capturing Earth images at varying scales. A machine learning processing circuitry employs a deep learning model with a Gaussian pyramid module to generate multi-scale feature maps. A scale selection block upsamples coarser maps to the finest scale for uniformity. Separate feature extractors convert these maps into vectors, which are combined via a scale attention module to compute attention weights. A cross-propagation feature module utilizes an uncertainty network to generate pixel-wise uncertainty maps, thereby guiding feature fusion into enhanced scale feature maps. A complexity-aware pooling mechanism preserves semantic features, which are then fed to a classification head that assigns class labels. The system outputs accurately labeled aerial images, enhancing robustness and precision in multi-scale aerial image recognition.