In　recent　years,　with　the　rapid　development　of　computer　software　and　hardware,　it　is　becoming　a　growing　number　of　popular　using　deep　learning　to　process　images.　Therefore,　many　scholars　also　focus　on　the　field　of　hyperspectral　image　(HSI)　classification　of　deep　neural　networks.　This　article　mainly　introduces　deep　neural　networks　in　HSI　processing,　including　stacked　auto-encoders,　deep　belief　networks,　and　convolutional　neural　networks　(CNNs).　At　the　same　time,　due　to　the　significant　advantages　of　CNNs　for　HSI　processing,　this　article　also　mainly　summarizes　the　methods　that　scholars　have　used　CNN　for　image　classification　over　the　year.　Meanwhile,　various　classification　networks　related　to　the　CNN　architecture　are　summarized.　After　that,　this　article　compares　the　advantages,　disadvantages,　and　characteristics　of　different　networks.　Finally,　combined　with　the　existing　problems,　some　future　directions　are　proposed　for　HSI　classification.　©　2021　IEEE.