A　robust　method　to　conduct　land　use　change　detection　between　multi-temporal　images　using　projection　pursuit　learning　network　architecture　(PPLNA)　is　proposed.　The　method　uses　a　parallel　approach　that　includes　three　different　PPLNs:　two　of　them　are　used　to　generate　the　change　map　using　the　multi-spectral　information,　while　the　third　produces　a　change　mask　exploiting　multi-temporality.　The　distinctive　feature　and　major　merit　of　PPLNA　from　traditional　neural　network　for　land　use　change　detection　are　the　proposed　method　simultaneously　exploits　both　the　post　classification　of　multi-spectral　and　multi-temporal　information　that　is　associated　with　the　changes　values　of　the　pixel　spectral　reflectance,　and　hence　improve　the　change　detection　accuracies.　To　validate　the　performance　of　the　proposed　method,　the　experiments　using　the　ETM+　images　for　the　area　of　Calgary　have　been　carried　out.　The　accuracies　of　the　final　classification　and　change　detection　maps　have　been　evaluated　with　ground　truth　comparisons.　The　experimental　result　demonstrates　that　the　proposed　method　achieves　better　accuracies.　©　2008　SPIE.