Existing　remote　sensing　scene　image　(RSSI)　classification　methods　usually　rely　on　static　closed-set　assumption　that　testing　samples　do　not　belong　to　unknown　classes.　However,　practical　applications　are　usually　the　open-set　classification　problem,　which　means　that　RSSIs　from　unknown　classes　will　appear　in　the　testing　set.　Most　existing　methods　are　prone　to　forcibly　misclassify　RSSIs　of　unknown　classes　into　known　classes,　resulting　in　poor　practical　performance.　In　this　letter,　a　deep　feature　reconstruction　learning　(DFRL)　framework　is　proposed　for　open-set　classification　of　RSSIs.　The　proposed　DFRL　unifies　discriminative　feature　learning　and　feature　reconstruction　into　an　end-to-end　network.　Firstly,　a　feature　extraction　module　is　utilized　to　project　raw　input　data　from　the　image　space　to　the　feature　space　to　extract　deep　features.　Then,　the　deep　features　are　fed　to　a　deep　feature　reconstruction　module　for　distinguishing　known　and　unknown　classes　based　on　feature-level　reconstruction　errors.　The　feature-level　reconstruction　can　effectively　suppress　the　interference　of　complex　backgrounds.　In　addition,　a　sparse　regularization　is　introduced　to　improve　the　discrimination　of　image　representation.　Experiments　on　three　RSSI　datasets　demonstrate　the　effectiveness　of　DFRL　for　open-set　classification　of　RSSIs.　IEEE