Landmark selection using homogeneity on nonlinear manifolds for unmixing hyperspectral data. - Junhwa Chi; Melba M. Crawford (IGARSS, 2012)

Selection of Landmark Points on Nonlinear Manifolds for Spectral Unmixing Using Local Homogeneity. - Junhwa Chi; Melba M. Crawford (IEEE Geosci. Remote. Sens. Lett., 2013)

Active Landmark Sampling for Manifold Learning Based Spectral Unmixing. - Junhwa Chi; Melba M. Crawford (IEEE Geosci. Remote. Sens. Lett., 2014)

Spectral Unmixing-Based Crop Residue Estimation Using Hyperspectral Remote Sensing Data: A Case Study at Purdue University. - Junhwa Chi; Melba M. Crawford (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2014)

Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network. - Junhwa Chi; Hyun-Cheol Kim (Remote. Sens., 2017)

Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction. - Junhwa Chi; Jihyun Bae; Young-Joo Kwon (Remote. Sens., 2021)

Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula. - Junhwa Chi; Hyoungseok Lee; Soon Gyu Hong; Hyun-Cheol Kim (Remote. Sens., 2021)

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models. - Hyeyeong Choe; Junhwa Chi; James H. Thorne (Remote. Sens., 2021)

An Optimal Image-Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images. - Pyung-Chae Lim; Sooahm Rhee; Junghoon Seo; Jae-In Kim; Junhwa Chi; Suk-bae Lee; Taejung Kim (Remote. Sens., 2021)

Mapping Capability of Hyperspectral Information on Dominant Arctic Vegetation Species Using Terrestrial Hyperspectral Imagery. - Junyoung Yang; Yoo Kyung Lee; Junhwa Chi (IGARSS, 2023)

Spectral unmixing-based Arctic plant species analysis using a spectral library and terrestrial hyperspectral Imagery: A case study in Adventdalen, Svalbard. - Junyoung Yang; Yoo Kyung Lee; Junhwa Chi (Int. J. Appl. Earth Obs. Geoinformation, 2023)