llegal gold mining in remote regions of the Brazilian Amazon poses great environmental and security threats. Developing detection systems for mining dredges is hampered by limited labeled data, often due to adverse weather. Despite the urgency, few studies have explored automated approaches using SAR imagery under low-data regimes. This work presents a automated pipeline for detecting mining dredges in ICEYE SAR images. The method begins by splitting full-scene GRD images into chips, classified using a Prototypical Network trained via Few-Shot Learning. A local range filter is then applied to enhance contrast, followed by threshold-based blob detection to localize potential dredges. The approach achieved recall rates between 79.7% and 93.2%, demonstrating competitive performance. Although it cannot yet distinguish dredges from other small vessels, its full automation and scalability make it suitable for large-scale monitoring of regions at risk of illegal mining.