Metric　learning　methods　have　been　widely　used　in　hyperspectral　image　(HSI)　classification.　They　can　project　higher　dimensional　feature　vectors　to　lower　dimensional　vectors　and　get　more　accurate　classification　results.　Recently,　nearest　feature　line　(NFL)　embedding　(NFLE)　algorithm　has　been　proposed　in　HSI　classification.　This　method　tries　to　embed　the　distance　between　a　point　and　its　NFL.　However,　the　decreasing　of　the　point-to-line　(P2L)　distance　does　not　mean　that　the　point-to-point　(P2P)　distance　decreases.　In　some　cases,　the　P2P　distance　may　even　increase,　which　results　in　poor　classification　performance.　In　this　letter,　amodified　algorithm　of　NFL　and　point　embedding　(NFLPE)　is　proposed　for　HSI　analysis.　Unlike　NFLE,　which　just　constrains　the　P2L　distance,　NFLPE　also　imposes　an　additional　constraint　on　the　P2P　distance.　This　additional　constraint　avoids　the　possibility　that　when　the　P2L　distance　decreases,　the　P2P　distance　increases.　Classification　experiments　with　HSI　demonstrate　its　superiority　to　other　related　techniques.